# mode: empirical discipline: interdisciplinary title: ""

# Research brief —

This file is the complete brief for one autonomous AutoScholar research run. Write it for a careful researcher who has never heard of the platform: everything they would need to know to carry out the study is here. Replace every .

# 1. Research objective and scope

Question. <One precise question, phrased so that it can be answered negatively.>
Why it matters.
Scope. <What is in and out: population, period, geography, model class, text corpus…>
Non-goals.

# 2. Domain, mode and methodology

Discipline(s): <economics | finance | mathematics | computer science | medicine | … >
Primary mode: <empirical | computational | theoretical | review | experimental | humanities>
Secondary modes (optional): <…>
Methodology. <Name the method and why it fits the question: e.g., panel regression with two-way fixed effects; proof by induction with a counterexample search; PRISMA-style systematic review; benchmark with 5 seeds and ablations.>

# 3. Proposed contribution and novelty assessment

Contribution.
Novelty check. <Which prior work to compare against; what would make the result "already known".> State novelty in qualified terms ("to our knowledge").

# 4. Literature and source rules

Search at least <2–3> indexes (OpenAlex, Crossref, arXiv, Semantic Scholar, PubMed as fits the field). Target <10–30> verified sources.
Cite only sources added through the platform's verification tool; prefer DOIs; read full texts where open access.
Required reading: <list any must-cite references with DOI/arXiv id>.
Record a claim-to-source map for every background claim used in the introduction and discussion.

# 5. Evidence: data, simulations, proofs or texts

Data sources (empirical): <official APIs/repositories, exact tables/series, period, license>. Document provenance, license, download commands, hashes.
Simulation (if any): <design, seeds, what it stands for>. Label synthetic output as synthetic.
Proof/formal objects (theoretical): <definitions, assumptions, candidate propositions>.
Corpus (review/humanities): <sources, editions, inclusion criteria>.

# 6. Procedure

<Step-by-step protocol: cleaning, estimation/derivation/screening, outputs.>

# 7. Baselines, robustness and falsification

Baselines/alternatives: <…>
Robustness: <alternative specifications, samples, hyper-parameters, relaxed assumptions, counterexample search>.
What would falsify the claim: .

# 8. Reproducibility and artifacts

All code under code/ with a pinned environment and code/reproduce.sh regenerating results/metrics.json, tables and figures from raw inputs.
All reported numbers registered with the platform's record_reported_value tool.
Data manifest with licenses and SHA-256; experiment log with commands, seeds, exit codes.

# 9. Manuscript structure and LaTeX standards

AutoScholar class (\documentclass{autoscholar}), natbib author-year, booktabs tables, vector figures.
Sections: abstract (150–300 words), introduction, related work, method/theory, data (if any), results, robustness, discussion incl. competing explanations, limitations, conclusion, references, appendices (proofs, protocol, reproducibility).
Target length: <8–20 pages>; depth over length.

# 10. Critical review, revision and quality expectations

The independent reviewer should focus on: <identification/validity threats, proof gaps, screening bias, …>.
Revise substantively; document a point-by-point response.
Hard requirements: verified citations, traceable numbers, honest limitations, clean build.

# 11. Limitations, ethics and legal constraints

<Ethical/legal constraints: no human-subject work, data licenses, no copyrighted full-text republication.>

# 12. Acceptance criteria

The run is successful only if: <concrete, checkable criteria — e.g., "the main specification and two robustness checks are estimated on the full sample; the paper reports effect sizes with confidence intervals; the proof of Proposition 1 is complete and checked symbolically; at least 15 verified sources are cited">.

spb/autoscholar