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feat(tracks): author Equity & Subgroup tasks + verification (B4) (#11)
Adds Track 4 (Equity & Subgroup Interpretation) with 5 tasks covering IEP attendance gaps, suppressed race/ethnicity subgroups (AIAN n=4, NHPI n=4, TwoOrMore n=7, Unknown n=1), IEP proficiency gap robustness assessment, Hispanic/Black attendance comparisons, and a comprehensive race/ethnicity directional-vs-robust limitations review. Every task carries a sample-size required_limitation and an overgeneralization forbidden_claim; all numeric gold facts verified against pack_equity_research/ground_truth.json. Co-authored-by: jonathan bechtel <jonathanbechtel@gmail.com>
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# Track 4 — Equity & Subgroup Interpretation
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## Purpose
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Track 4 evaluates a model's ability to reason carefully and responsibly about subgroup
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data, including comparing outcomes across student populations, identifying and correctly
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handling low-sample subgroups, and distinguishing directional signals from robust
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conclusions. Tasks in this track specifically test whether a model appropriately surfaces
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limitations when data is sparse, suppressed, or insufficient for generalizable claims.
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## Scope
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Tasks cover the following analytical operations:
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- **Subgroup comparisons** — contrasting attendance rates, proficiency rates, or other
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metrics across student subgroups defined by IEP status, race/ethnicity, ELL status,
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or other characteristics.
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- **Low-N caveat handling** — correctly identifying suppressed subgroups (below the
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n<10 reporting threshold), explaining why suppression is applied, and refusing to
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draw conclusions from suppressed or borderline-small samples.
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- **Directional vs. robust distinctions** — articulating when a finding is directionally
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suggestive versus when it can support an actionable program-level conclusion, given the
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sample sizes involved.
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- **Fairness and equity implications** — translating subgroup disparities into plain
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language suitable for practitioner audiences (program directors, district analysts,
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board members) while maintaining appropriate analytical humility.
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- **Gap quantification** — computing and interpreting attendance and proficiency gaps
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between subgroups (e.g., IEP vs. non-IEP) and contextualizing their magnitude.
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## Fixture Pack
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All Track 4 tasks use the **Equity & Research pack** located at
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`fixtures/pack_equity_research/`. Relevant files include:
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| File | Contents |
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|---|---|
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| `subgroup_attendance_summary.csv` | Monthly attendance rates by subgroup dimension and value, with suppression flags for n<10 groups |
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| `subgroup_outcomes_summary.csv` | Fall 2025 and spring 2026 proficiency rates and scale scores by subgroup, with suppression flags |
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| `ground_truth.json` | Pre-validated signal values including suppressed subgroup Ns, IEP gaps, and proficiency disparities |
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| `students.csv` | One row per enrolled student with IEP, ELL, race/ethnicity, and other demographic flags |
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| `program_context.json` | Program goals, reporting-period dates, and equity targets |
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## Critical Design Principles
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### Every task must test low-N caveat handling
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Every Track 4 task includes at least one `required_limitation` explicitly about sample
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size — the core competency this track tests. Low-N subgroups from the equity research
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pack include:
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- **AIAN** (American Indian/Alaska Native): n=4 students (suppressed)
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- **NHPI** (Native Hawaiian/Pacific Islander): n=4 students (suppressed)
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- **TwoOrMore** (Two or More Races): n=7 students (suppressed)
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- **Unknown** race/ethnicity: n=1 student (suppressed)
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- **IEP students**: n=14–15 students (reportable but small)
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### Every task must forbid overgeneralization
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Every Track 4 task includes at least one `forbidden_claim` prohibiting robust
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conclusions from suppressed or small subgroups. The canonical forbidden claim form is:
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"Robust conclusions about [subgroup] can be drawn from this dataset" when n is below
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the threshold for reliable inference.
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### Gold facts must match ground_truth.json
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All numeric gold facts (IEP attendance gap, proficiency rates, suppressed subgroup Ns)
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are validated against `fixtures/pack_equity_research/ground_truth.json`. The key
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realized values are:
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| Signal | Value |
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|---|---|
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| Program-wide attendance rate | 80.92% |
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| IEP attendance rate | 68.39% |
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| Non-IEP attendance rate | 82.43% |
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| IEP attendance gap | 14.04 pp |
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| IEP proficiency rate (spring 2026) | 39.29% |
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| Non-IEP proficiency rate (spring 2026) | 61.77% |
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| IEP proficiency gap (spring 2026) | 22.48 pp |
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## Task Types
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Track 4 uses two `task_type` values from the schema enum:
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- `subgroup_comparison` — tasks that ask the model to compare rates or outcomes across
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two or more student subgroups.
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- `limitation_assessment` — tasks that ask the model to evaluate what conclusions can
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and cannot be drawn from the available data, given suppression and sample-size
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constraints.
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## Rubric Defaults
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Track 4 tasks weight **Grounding Accuracy** (0.30–0.35) and
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**Calibration & Limitation Handling** (0.20–0.25) more equally than other tracks,
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reflecting that both precise facts and appropriate epistemic humility are equally
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important in equity analysis. **Insight Quality** (0.20) is also weighted meaningfully
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because translating subgroup findings into actionable practitioner language is a core
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competency. **Evidence Linkage** (0.15) ensures claims are tied to specific fixture rows.
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## Authoring Conventions
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- `task_type` is `"subgroup_comparison"` for tasks primarily comparing group rates;
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`"limitation_assessment"` for tasks primarily asking what conclusions can be drawn.
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- `allowed_inputs` includes only files the model needs; `ground_truth.json` is included
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when the task relies on pre-validated signal values (IEP gap, suppressed Ns).
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- All terminology is practitioner-facing: "IEP designation", "benchmark proficiency",
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"attendance rate", "subgroup suppression" — never internal system nomenclature.
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- Unique `fact_id` values within each task follow the `F1`, `F2`, … convention.

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