From dac00f90dbd84c85d415924ce32994628a84384d Mon Sep 17 00:00:00 2001
From: Joey Eamigh <55670930+JoeyEamigh@users.noreply.github.com>
Date: Sun, 5 Apr 2026 18:44:40 -0400
Subject: [PATCH] data: 246 new

---
 .lfs-manifest                                 | 246 ++++++++++++++++++
 .../checkpoint-1024/config.json.zst           |   3 +
 .../checkpoint-1024/model.safetensors.zst     |   3 +
 .../checkpoint-1024/tokenizer.json.zst        |   3 +
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 labelapp/app/codebook/page.tsx                | 124 +++++----
 labelapp/lib/warmup-paragraphs.ts             |   4 +-
 python/src/data/__init__.py                   |   0
 python/src/data/corpus.py                     | 141 ++++++++++
 ts/data/pilot/pilot-report.txt                |  66 +++++
 ts/data/pilot/pilot-sample.jsonl              |   1 +
 253 files changed, 1268 insertions(+), 52 deletions(-)
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index 0000000..17802c6
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-4278/training_args.bin.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3667722a3760ac1964a6d071fb0c9173c9eb125b85b1a2acb219f37cec8d4251
+size 2465
diff --git a/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/config.json.zst b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/config.json.zst
new file mode 100644
index 0000000..5deda10
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/config.json.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c866577b99b4ce148a1c542b3fe4805114dce64d1e1b98ee295363eebd8b2d96
+size 600
diff --git a/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/model.safetensors.zst b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/model.safetensors.zst
new file mode 100644
index 0000000..31937ec
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/model.safetensors.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b2fefdb3e2a1c1cd110803851d6af70e29e6b207902ffb3a23af711766032de2
+size 600554186
diff --git a/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/tokenizer.json.zst b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/tokenizer.json.zst
new file mode 100644
index 0000000..b10d2fa
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/tokenizer.json.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3fefe452ddf3fe7319f3a115ca178f8e51123b0fbe15e8b6f587520e9582739
+size 516851
diff --git a/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/tokenizer_config.json.zst b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/tokenizer_config.json.zst
new file mode 100644
index 0000000..79d225e
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/tokenizer_config.json.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e80c44068f8013859cf75550f0fd8249bd7e839e2d4907ef45f667ad55fea28
+size 215
diff --git a/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/trainer_state.json.zst b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/trainer_state.json.zst
new file mode 100644
index 0000000..f48a977
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/trainer_state.json.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0809885244baac41a61716e61e62b5904a5000b5998efda3ff1d9c59d228229c
+size 4325
diff --git a/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/training_args.bin.zst b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/training_args.bin.zst
new file mode 100644
index 0000000..17802c6
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-6417/training_args.bin.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3667722a3760ac1964a6d071fb0c9173c9eb125b85b1a2acb219f37cec8d4251
+size 2465
diff --git a/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/config.json.zst b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/config.json.zst
new file mode 100644
index 0000000..5deda10
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/config.json.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c866577b99b4ce148a1c542b3fe4805114dce64d1e1b98ee295363eebd8b2d96
+size 600
diff --git a/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/model.safetensors.zst b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/model.safetensors.zst
new file mode 100644
index 0000000..5bf1a6b
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/model.safetensors.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4efb11359af0e8bc6593835c8febbd40e0095c8b96185eb136bf236e8a87296e
+size 599717976
diff --git a/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/tokenizer.json.zst b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/tokenizer.json.zst
new file mode 100644
index 0000000..b10d2fa
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/tokenizer.json.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3fefe452ddf3fe7319f3a115ca178f8e51123b0fbe15e8b6f587520e9582739
+size 516851
diff --git a/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/tokenizer_config.json.zst b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/tokenizer_config.json.zst
new file mode 100644
index 0000000..79d225e
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/tokenizer_config.json.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e80c44068f8013859cf75550f0fd8249bd7e839e2d4907ef45f667ad55fea28
+size 215
diff --git a/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/trainer_state.json.zst b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/trainer_state.json.zst
new file mode 100644
index 0000000..dc5f533
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/trainer_state.json.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a32eae5b99581bd9bb4176c8f6468ae4d39dc9d1742ca993e630983e8ab5d880
+size 5506
diff --git a/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/training_args.bin.zst b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/training_args.bin.zst
new file mode 100644
index 0000000..17802c6
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/checkpoint-8556/training_args.bin.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3667722a3760ac1964a6d071fb0c9173c9eb125b85b1a2acb219f37cec8d4251
+size 2465
diff --git a/.lfs-store/checkpoints/tapt/modernbert-large/final/config.json.zst b/.lfs-store/checkpoints/tapt/modernbert-large/final/config.json.zst
new file mode 100644
index 0000000..5deda10
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/final/config.json.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c866577b99b4ce148a1c542b3fe4805114dce64d1e1b98ee295363eebd8b2d96
+size 600
diff --git a/.lfs-store/checkpoints/tapt/modernbert-large/final/model.safetensors.zst b/.lfs-store/checkpoints/tapt/modernbert-large/final/model.safetensors.zst
new file mode 100644
index 0000000..5bf1a6b
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/final/model.safetensors.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4efb11359af0e8bc6593835c8febbd40e0095c8b96185eb136bf236e8a87296e
+size 599717976
diff --git a/.lfs-store/checkpoints/tapt/modernbert-large/final/tokenizer.json.zst b/.lfs-store/checkpoints/tapt/modernbert-large/final/tokenizer.json.zst
new file mode 100644
index 0000000..b10d2fa
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/final/tokenizer.json.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3fefe452ddf3fe7319f3a115ca178f8e51123b0fbe15e8b6f587520e9582739
+size 516851
diff --git a/.lfs-store/checkpoints/tapt/modernbert-large/final/tokenizer_config.json.zst b/.lfs-store/checkpoints/tapt/modernbert-large/final/tokenizer_config.json.zst
new file mode 100644
index 0000000..79d225e
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/final/tokenizer_config.json.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e80c44068f8013859cf75550f0fd8249bd7e839e2d4907ef45f667ad55fea28
+size 215
diff --git a/.lfs-store/checkpoints/tapt/modernbert-large/final/training_args.bin.zst b/.lfs-store/checkpoints/tapt/modernbert-large/final/training_args.bin.zst
new file mode 100644
index 0000000..17802c6
--- /dev/null
+++ b/.lfs-store/checkpoints/tapt/modernbert-large/final/training_args.bin.zst
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3667722a3760ac1964a6d071fb0c9173c9eb125b85b1a2acb219f37cec8d4251
+size 2465
diff --git a/labelapp/app/codebook/page.tsx b/labelapp/app/codebook/page.tsx
index 6a1497c..5b1f085 100644
--- a/labelapp/app/codebook/page.tsx
+++ b/labelapp/app/codebook/page.tsx
@@ -130,6 +130,15 @@ export default function CodebookPage() {
               <strong>Specificity Level</strong> — ordinal integer 1&ndash;4
             </li>
           </ol>
+          <p className="rounded-md border border-blue-300 bg-blue-50 dark:border-blue-700 dark:bg-blue-950/30 p-3 text-sm">
+            <strong>Category and specificity are independent dimensions.</strong>{" "}
+            Category captures <em>what</em> the paragraph discloses. Specificity
+            captures <em>how company-specific</em> it is. A materiality
+            disclaimer can be Strategy Integration at Level 1 (generic
+            boilerplate). A Board Governance paragraph that mentions CrowdStrike
+            Falcon is Level 4. Don&rsquo;t let the category influence your
+            specificity rating or vice versa — assign each independently.
+          </p>
           <p>
             <strong>None/Other policy:</strong> Required. Since this is
             multi-class (not multi-label), we need a catch-all for paragraphs
@@ -175,16 +184,20 @@ export default function CodebookPage() {
                 &ldquo;board committee&rdquo;
               </li>
               <li>
-                <strong>Assign when:</strong> The grammatical subject performing
-                the primary action is the board or a board committee
+                <strong>Assign when:</strong> The paragraph answers &ldquo;How
+                does the board oversee cybersecurity?&rdquo; — the board or a
+                board committee is overseeing, receiving reports, or delegating.
+                Governance-chain paragraphs (Board &rarr; Committee &rarr;
+                Officer &rarr; Program) default to BG when the purpose is
+                describing the oversight structure.
               </li>
             </ul>
 
             <ExampleBlock
               text="The Board of Directors oversees the Company's management of cybersecurity risks. The Board has delegated oversight of cybersecurity and data privacy matters to the Audit Committee."
               category="Board Governance"
-              specificity={3}
-              explanation="Names Audit Committee — firm-specific delegation"
+              specificity={1}
+              explanation="Board of Directors and Audit Committee are both on the NOT list (every public company has them). No domain terminology, no firm-specific facts, no QV facts."
             />
             <ExampleBlock
               text="Our Board of Directors recognizes the critical importance of maintaining the trust and confidence of our customers and stakeholders, and cybersecurity risk is an area of increasing focus for our Board."
@@ -195,8 +208,8 @@ export default function CodebookPage() {
             <ExampleBlock
               text="The Audit Committee, which includes two members with significant technology and cybersecurity expertise, receives quarterly reports from the CISO and conducts an annual deep-dive review of the Company's cybersecurity program, threat landscape, and incident response readiness."
               category="Board Governance"
-              specificity={3}
-              explanation="Names specific committee, describes specific briefing cadence and scope"
+              specificity={4}
+              explanation="'Two members' is a specific headcount (QV-eligible). CISO is firm-specific (IS list). Specificity rates the whole paragraph — category is BG but the QV fact still counts."
             />
           </div>
 
@@ -208,41 +221,42 @@ export default function CodebookPage() {
             <ul className="list-disc list-inside space-y-1 text-sm">
               <li><strong>SEC basis:</strong> Item 106(c)(2)</li>
               <li>
-                <strong>Covers:</strong> The specific <em>person</em> filling a
-                cybersecurity leadership position: their name, qualifications,
-                career history, credentials, tenure, reporting lines, management
-                committees responsible for cybersecurity
+                <strong>Covers:</strong> How management is organized to handle
+                cybersecurity — leadership roles and responsibilities,
+                qualifications and credentials, career history and experience,
+                management-level committee structure and membership, reporting
+                lines between management roles, team composition and size
               </li>
               <li>
-                <strong>Key markers:</strong> &ldquo;Chief Information Security
-                Officer,&rdquo; &ldquo;reports to,&rdquo; &ldquo;years of
+                <strong>Key markers:</strong> &ldquo;CISO,&rdquo;
+                &ldquo;reports to the CIO,&rdquo; &ldquo;years of
                 experience,&rdquo; &ldquo;management committee,&rdquo;
                 &ldquo;CISSP,&rdquo; &ldquo;CISM,&rdquo; named individuals,
-                career background
+                career background, team size, role allocation
               </li>
               <li>
-                <strong>Assign when:</strong> The paragraph tells you something
-                about <em>who the person is</em> — their background,
-                credentials, experience, or reporting structure. A paragraph that
+                <strong>Assign when:</strong> The paragraph&rsquo;s primary
+                content is about management&rsquo;s organizational role in
+                cybersecurity — who holds responsibilities, how those
+                responsibilities are divided, what qualifies those people, or
+                how management-level oversight is structured. A paragraph that
                 names a CISO/CIO/CTO and then describes what the cybersecurity{" "}
                 <em>program</em> does is NOT Management Role — it is Risk
-                Management Process with an incidental role attribution. The test
-                is whether the paragraph is about the <strong>person</strong> or
-                about the <strong>function</strong>.
+                Management Process with an incidental role attribution.
               </li>
             </ul>
 
             <div className="rounded-md border border-border bg-muted/30 p-4 text-sm">
-              <p className="font-semibold">The person-vs-function test:</p>
+              <p className="font-semibold">The person-removal test (confirmation tool):</p>
               <p className="mt-1">
-                If you removed the role holder&rsquo;s name, title,
-                qualifications, and background from the paragraph and the
-                remaining content still describes substantive cybersecurity
-                activities, processes, or oversight &rarr; the paragraph is about
-                the function (Risk Management Process), not the person
-                (Management Role). Management Role requires the person&rsquo;s
-                identity or credentials to be the primary content, not just a
-                brief attribution of who runs the program.
+                Remove all person-specific content (names, titles,
+                qualifications, experience, reporting lines, team composition,
+                committee membership). If the remaining text still describes a
+                substantive cybersecurity program &rarr; <strong>RMP</strong>.
+                If the paragraph collapses to near-nothing &rarr;{" "}
+                <strong>MR</strong>. This test confirms what the &ldquo;what
+                question?&rdquo; test already suggests — use it when you
+                hesitate, not as the primary rule.
               </p>
             </div>
 
@@ -267,8 +281,8 @@ export default function CodebookPage() {
             <ExampleBlock
               text="Our CISO oversees the Company's cybersecurity program, which includes risk assessments, vulnerability scanning, penetration testing, and incident response planning aligned with the NIST CSF framework."
               category="Risk Management Process"
-              specificity={2}
-              explanation="NOT Management Role — The CISO is mentioned once as attribution, but the paragraph is about what the program does. Remove 'Our CISO oversees' and the paragraph still makes complete sense as a process description."
+              specificity={3}
+              explanation="NOT Management Role — The CISO is mentioned once as attribution, but the paragraph is about what the program does. Remove 'Our CISO oversees' and the paragraph still makes complete sense as a process description. Specificity: CISO is on the IS list (firm-specific) — specificity rates the WHOLE paragraph, not just the category-relevant parts."
             />
           </div>
 
@@ -394,9 +408,9 @@ export default function CodebookPage() {
 
             <ExampleBlock
               text="We have experienced, and may in the future experience, cybersecurity incidents that could have a material adverse effect on our business, results of operations, and financial condition."
-              category="Incident Disclosure"
+              category="None/Other"
               specificity={1}
-              explanation="Hypothetical — no actual incident described. Note: if this appears in Item 1C rather than an 8-K, consider None/Other instead since it's generic risk language"
+              explanation="NOT Incident Disclosure — no actual incident is described. 'Could have a material adverse effect' is speculation, not a materiality assessment. This is generic risk language that appears in every 10-K. Incident Disclosure is reserved for events that actually occurred."
             />
             <ExampleBlock
               text="On January 15, 2024, we detected unauthorized access to our customer support portal. The threat actor exploited a known vulnerability in a third-party software component. Upon detection, we activated our incident response plan, contained the intrusion, and engaged Mandiant for forensic investigation."
@@ -407,8 +421,8 @@ export default function CodebookPage() {
             <ExampleBlock
               text="In December 2023, the Company experienced a cybersecurity incident involving unauthorized access to certain internal systems. The Company promptly took steps to contain and remediate the incident, including engaging third-party cybersecurity experts."
               category="Incident Disclosure"
-              specificity={3}
-              explanation="Specific month, specific action — but no named firms or quantified impact"
+              specificity={4}
+              explanation="December 2023 is a specific date (month+year) tied to a cybersecurity event — QV-eligible. 'Third-party cybersecurity experts' is unnamed (NOT QV), but the date alone is enough for Level 4."
             />
           </div>
 
@@ -464,8 +478,8 @@ export default function CodebookPage() {
             <ExampleBlock
               text="We maintain cybersecurity insurance coverage as part of our overall risk management strategy to help mitigate potential financial losses from cybersecurity incidents."
               category="Strategy Integration"
-              specificity={2}
-              explanation="Mentions insurance but no specifics"
+              specificity={1}
+              explanation="Generic business language — 'insurance coverage,' 'risk management strategy,' 'financial losses' are all general terms. No domain terminology, no firm-specific facts, no QV facts."
             />
             <ExampleBlock
               text="We increased our cybersecurity budget by 32% to $45M in fiscal 2024, representing 0.8% of revenue. We maintain cyber liability insurance with $100M in aggregate coverage through AIG and Chubb, with a $5M deductible per incident."
@@ -587,7 +601,7 @@ export default function CodebookPage() {
               </TableHeader>
               <TableBody>
                 <TableRow>
-                  <TableCell>Board/committee is the grammatical subject</TableCell>
+                  <TableCell>Board/committee oversees, receives reports, delegates</TableCell>
                   <TableCell>Board Governance</TableCell>
                 </TableRow>
                 <TableRow>
@@ -595,20 +609,28 @@ export default function CodebookPage() {
                   <TableCell>Board Governance</TableCell>
                 </TableRow>
                 <TableRow>
-                  <TableCell>Management role reports TO the board</TableCell>
+                  <TableCell>Management reports TO the board (describing oversight flow)</TableCell>
+                  <TableCell>Board Governance</TableCell>
+                </TableRow>
+                <TableRow>
+                  <TableCell>Management roles, responsibilities, how they&rsquo;re divided</TableCell>
                   <TableCell>Management Role</TableCell>
                 </TableRow>
                 <TableRow>
-                  <TableCell>Management role&rsquo;s qualifications are described</TableCell>
+                  <TableCell>Person&rsquo;s qualifications, credentials, experience</TableCell>
+                  <TableCell>Management Role</TableCell>
+                </TableRow>
+                <TableRow>
+                  <TableCell>Management-level committee structure and membership</TableCell>
                   <TableCell>Management Role</TableCell>
                 </TableRow>
                 <TableRow>
                   <TableCell>&ldquo;Board oversees... CISO reports to Board quarterly&rdquo;</TableCell>
-                  <TableCell>Board Governance (board is primary actor)</TableCell>
+                  <TableCell>Board Governance (oversight flow)</TableCell>
                 </TableRow>
                 <TableRow>
                   <TableCell>&ldquo;CISO reports quarterly to the Board on...&rdquo;</TableCell>
-                  <TableCell>Management Role (CISO is primary actor)</TableCell>
+                  <TableCell>Board Governance (board is the recipient — still describing oversight)</TableCell>
                 </TableRow>
               </TableBody>
             </Table>
@@ -621,7 +643,7 @@ export default function CodebookPage() {
             </SectionHeading>
             <p>
               This is the single most common source of annotator disagreement.
-              The line is: <strong>is the paragraph about the person or about the function?</strong>
+              The line is: <strong>is the paragraph about management&rsquo;s organizational role, or about what the program does?</strong>
             </p>
             <Table>
               <TableHeader>
@@ -667,9 +689,10 @@ export default function CodebookPage() {
                 paragraph&rsquo;s substantive content is about processes,
                 activities, or tools, it is Risk Management Process regardless of
                 how many times a role title appears. Management Role requires the
-                paragraph&rsquo;s content to be about the <em>person</em> — who
-                they are, what makes them qualified, how long they&rsquo;ve
-                served, what their background is.
+                paragraph&rsquo;s content to be about management&rsquo;s{" "}
+                <em>organizational role</em> — who holds responsibilities, how
+                roles are divided, what qualifies those people, or how
+                management-level structures work.
               </p>
             </div>
           </div>
@@ -812,9 +835,9 @@ export default function CodebookPage() {
             </SectionHeading>
             <ol className="list-decimal list-inside space-y-2 text-sm">
               <li>
-                <strong>Count hard verifiable facts ONLY</strong> (specific
-                dates, dollar amounts, headcounts/percentages, named third-party
-                firms, named products/tools, named certifications). At least ONE?
+                <strong>Does it contain at least one QV-eligible fact?</strong>{" "}
+                (specific dates, dollar amounts, headcounts/percentages, named
+                third-party firms, named products/tools, named certifications)
                 &rarr; <strong>Quantified-Verifiable (4)</strong>
               </li>
               <li>
@@ -1071,8 +1094,9 @@ export default function CodebookPage() {
               framework alignment, ERM integration. Remove &ldquo;Our CISO
               oversees&rdquo; and it still makes complete sense as a process
               description. &rarr;{" "}
-              <strong>Risk Management Process, Specificity 2</strong> (NIST CSF
-              framework, no firm-specific facts beyond that).
+              <strong>Risk Management Process, Specificity 3</strong> (CISO is
+              on the IS list — specificity rates the whole paragraph, not just
+              the category-relevant parts).
             </p>
 
             <div className="rounded-md border border-border bg-muted/50 p-4">
diff --git a/labelapp/lib/warmup-paragraphs.ts b/labelapp/lib/warmup-paragraphs.ts
index 511c455..efaca78 100644
--- a/labelapp/lib/warmup-paragraphs.ts
+++ b/labelapp/lib/warmup-paragraphs.ts
@@ -11,9 +11,9 @@ export const WARMUP_PARAGRAPHS: WarmupParagraph[] = [
     id: "warmup-1",
     text: "The Board of Directors oversees the Company's management of cybersecurity risks. The Board has delegated oversight of cybersecurity and data privacy matters to the Audit Committee, which receives quarterly reports from management on the Company's cybersecurity risk management program, recent threats, and any incidents.",
     goldCategory: "Board Governance",
-    goldSpecificity: 3,
+    goldSpecificity: 1,
     explanation:
-      "Board Governance because the Board of Directors and Audit Committee are the grammatical subjects performing the primary actions (overseeing, delegating, receiving reports). Specificity 3 (Firm-Specific) because the paragraph describes a specific delegation structure (to the Audit Committee) with a defined briefing cadence. While 'Audit Committee' alone is generic (NOT list), the delegation of cybersecurity oversight to it is a firm-specific organizational choice. No QV-eligible facts present (no specific numbers, dates, named entities, or tools).",
+      "Board Governance because the paragraph answers 'How does the board oversee cybersecurity?' — the Board and Audit Committee are overseeing, delegating, and receiving reports. Specificity 1 (Generic Boilerplate) because every term is on the NOT list: 'Board of Directors,' 'Audit Committee' (every public company has one), 'management,' and 'quarterly' (generic cadence). No cybersecurity domain terminology, no firm-specific facts (IS list), no QV-eligible facts.",
   },
   {
     id: "warmup-2",
diff --git a/python/src/data/__init__.py b/python/src/data/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/src/data/corpus.py b/python/src/data/corpus.py
new file mode 100644
index 0000000..7e4ca0d
--- /dev/null
+++ b/python/src/data/corpus.py
@@ -0,0 +1,141 @@
+"""DAPT/TAPT corpus loading and tokenization.
+
+Loads clean text from JSONL (sharded directory or single file),
+tokenizes, and concatenates + chunks into fixed-length sequences
+for masked language modeling.
+"""
+
+from os import cpu_count
+from pathlib import Path
+
+from datasets import Dataset, load_dataset
+from transformers import PreTrainedTokenizerBase
+
+
+def load_corpus(corpus_path: str, text_field: str = "text") -> Dataset:
+    """Load JSONL file(s) into a HuggingFace Dataset.
+
+    Args:
+        corpus_path: Path to a single JSONL file or a directory of JSONL shards.
+        text_field: Name of the JSON field containing the text.
+
+    Returns:
+        A Dataset with at least a `text_field` column.
+    """
+    p = Path(corpus_path)
+    if p.is_file():
+        ds = load_dataset("json", data_files=str(p), split="train")
+    elif p.is_dir():
+        ds = load_dataset("json", data_dir=str(p), split="train")
+    else:
+        raise FileNotFoundError(f"Corpus path not found: {corpus_path}")
+
+    if text_field not in ds.column_names:
+        raise ValueError(
+            f"Text field '{text_field}' not found. "
+            f"Available columns: {ds.column_names}"
+        )
+    return ds
+
+
+def tokenize_and_chunk(
+    dataset: Dataset,
+    tokenizer: PreTrainedTokenizerBase,
+    text_field: str = "text",
+    max_seq_length: int = 2048,
+    whole_word_mask: bool = False,
+) -> Dataset:
+    """Tokenize texts for MLM pre-training.
+
+    Two modes:
+    - **DAPT** (whole_word_mask=False): Concatenate all tokens and chunk into
+      fixed-length sequences. Standard for pre-training on long documents —
+      maximizes GPU utilization by avoiding padding.
+    - **TAPT** (whole_word_mask=True): Tokenize each text individually with
+      truncation, preserving word_ids for whole-word masking. The data
+      collator handles dynamic padding per batch.
+
+    Args:
+        dataset: Dataset with a text column.
+        tokenizer: Pretrained tokenizer.
+        text_field: Column name containing raw text.
+        max_seq_length: Length of each output sequence.
+        whole_word_mask: If True, return per-sample word_ids instead
+            of concatenate-and-chunk. Required for whole-word masking.
+
+    Returns:
+        Dataset with `input_ids` and `attention_mask` columns.
+        If whole_word_mask=True, also includes `word_ids`.
+    """
+    nproc = cpu_count() or 1
+
+    if whole_word_mask:
+        # Tokenize each paragraph individually, extracting word_ids for
+        # whole-word masking. We use word_ids() (from the fast tokenizer)
+        # instead of offset_mapping because BPE tokenizers absorb leading
+        # spaces into tokens, making all offsets contiguous — the upstream
+        # offset_mapping-based boundary detection treats entire sequences
+        # as one "word".
+        def tokenize_fn(examples: dict) -> dict:
+            encodings = tokenizer(
+                examples[text_field],
+                truncation=True,
+                max_length=max_seq_length,
+            )
+            batch_word_ids = []
+            for i in range(len(examples[text_field])):
+                wids = encodings.word_ids(i)
+                # None (special tokens) → -1 for Arrow integer storage
+                batch_word_ids.append([w if w is not None else -1 for w in wids])
+            return {
+                "input_ids": encodings["input_ids"],
+                "attention_mask": encodings["attention_mask"],
+                "word_ids": batch_word_ids,
+            }
+
+        return dataset.map(
+            tokenize_fn,
+            batched=True,
+            batch_size=100,
+            num_proc=nproc,
+            remove_columns=dataset.column_names,
+            desc="Tokenizing (whole-word mask)",
+        )
+
+    # DAPT mode: concatenate-then-chunk
+    def tokenize_fn(examples: dict) -> dict:
+        return tokenizer(
+            examples[text_field],
+            truncation=False,
+            add_special_tokens=False,
+        )
+
+    tokenized = dataset.map(
+        tokenize_fn,
+        batched=True,
+        batch_size=100,
+        num_proc=nproc,
+        remove_columns=dataset.column_names,
+        desc="Tokenizing",
+    )
+
+    # Concatenate-then-chunk in parallel batches. Each batch of documents
+    # has its tokens concatenated and sliced into fixed-length sequences.
+    # Only the tail of each batch is dropped (not each document), so token
+    # waste is negligible (~nproc * max_seq_length tokens total).
+    def chunk_fn(examples: dict) -> dict:
+        all_ids: list[int] = []
+        all_mask: list[int] = []
+        for ids, mask in zip(examples["input_ids"], examples["attention_mask"]):
+            all_ids.extend(ids)
+            all_mask.extend(mask)
+        n = (len(all_ids) // max_seq_length) * max_seq_length
+        return {
+            "input_ids": [all_ids[i : i + max_seq_length] for i in range(0, n, max_seq_length)],
+            "attention_mask": [all_mask[i : i + max_seq_length] for i in range(0, n, max_seq_length)],
+        }
+
+    return tokenized.map(
+        chunk_fn, batched=True, batch_size=500, num_proc=nproc,
+        remove_columns=tokenized.column_names, desc="Chunking",
+    )
diff --git a/ts/data/pilot/pilot-report.txt b/ts/data/pilot/pilot-report.txt
new file mode 100644
index 0000000..d9d2aed
--- /dev/null
+++ b/ts/data/pilot/pilot-report.txt
@@ -0,0 +1,66 @@
+SEC-cyBERT Prompt Pilot Report — 2026-03-28T05:14:43.318Z
+Prompt version: v1.0
+Sample: 0 paragraphs, seed=42
+Models: google/gemini-3.1-flash-lite-preview, xiaomi/mimo-v2-flash, x-ai/grok-4.1-fast
+Total cost: $0.0000
+
+═══ PER-MODEL STATS ═══
+
+google/gemini-3.1-flash-lite-preview:
+  Cost: $0.0000 ($NaN/para)
+  Tokens: 0 in, 0 out, 0 reasoning
+  Avg latency: 0ms
+  Categories: 
+  Specificity: 
+  Category confidence:    
+  Specificity confidence: 
+
+xiaomi/mimo-v2-flash:
+  Cost: $0.0000 ($NaN/para)
+  Tokens: 0 in, 0 out, 0 reasoning
+  Avg latency: 0ms
+  Categories: 
+  Specificity: 
+  Category confidence:    
+  Specificity confidence: 
+
+x-ai/grok-4.1-fast:
+  Cost: $0.0000 ($NaN/para)
+  Tokens: 0 in, 0 out, 0 reasoning
+  Avg latency: 0ms
+  Categories: 
+  Specificity: 
+  Category confidence:    
+  Specificity confidence: 
+
+
+═══ AGREEMENT ANALYSIS ═══
+Paragraphs with all 3 models: 0
+
+Content Category Agreement:
+  3/3 unanimous: 0/0 (NaN%)
+  2/3 majority:  0/0 (NaN%)
+  All disagree:  0/0 (NaN%)
+
+Specificity Level Agreement:
+  3/3 unanimous: 0/0 (NaN%)
+  2/3 majority:  0/0 (NaN%)
+  All disagree:  0/0 (NaN%)
+
+Both dimensions 3/3: 0/0 (NaN%)
+
+Pairwise category agreement:
+  gemini-3.1-flash-lite-preview × mimo-v2-flash: 0/0 (NaN%)
+  gemini-3.1-flash-lite-preview × grok-4.1-fast: 0/0 (NaN%)
+  mimo-v2-flash × grok-4.1-fast: 0/0 (NaN%)
+
+
+═══ DISAGREEMENT DETAILS ═══
+
+
+═══ COST PROJECTIONS (50K paragraphs) ═══
+  TOTAL Stage 1 (all 3 models): ~$NaN
+
+  Observed disagreement rate: NaN%
+  Estimated Stage 2 judge calls: ~NaN
+  (Judge cost depends on Sonnet 4.6 pricing — see OpenRouter)
\ No newline at end of file
diff --git a/ts/data/pilot/pilot-sample.jsonl b/ts/data/pilot/pilot-sample.jsonl
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/ts/data/pilot/pilot-sample.jsonl
@@ -0,0 +1 @@
+