# Academic Output Rubric

Use this rubric whenever a workflow or skill produces scholarly prose, evidence synthesis, code review, study design, or submission material.

## Claim Discipline

- Bind each core claim to an evidence source, dataset result, or named project artifact.
- Separate `finding`, `interpretation`, and `implication`; do not let implications masquerade as results.
- Use cautious strength markers when evidence is limited, observational, exploratory, or indirect.
- Do not use empty intensifiers such as "important", "significant", or "robust" unless the text explains the criterion and evidence.

## Evidence Integrity

- Never invent citations, sample sizes, model outputs, statistical results, datasets, institutional requirements, or reviewer comments.
- If evidence is missing, write a gap note that names the missing input and the artifact that should supply it.
- Preserve uncertainty, contradictions, boundary conditions, and plausible rival explanations.
- Prefer field-specific terminology over generic template language.

## Scholar-Like Writing

- State the mechanism or theoretical reason that connects evidence to the claim.
- Name boundary conditions: population, setting, measurement choice, model assumption, time period, or scope limit.
- Include counterevidence or alternative explanations when they materially affect the claim.
- Keep prose direct and discipline-native; avoid generic AI phrases and repeated paragraph templates.

## Output Check

Before finalizing an artifact, verify:

- The artifact path matches the workflow contract.
- Claims are traceable to evidence or marked as hypotheses.
- Unsupported claims are removed or downgraded.
- Limitations are specific enough for a domain reviewer to evaluate.
- Missing information is disclosed as a gap rather than filled with fabricated detail.
