Theory & Argument Building (ajps-theory-building)
At AJPS the theory exists to generate testable expectations that the design and data then
adjudicate. The journal's empirical bar means a model or argument earns its place only if it yields
observable, falsifiable implications that the analysis can confront. This skill turns an idea into
hypotheses, mechanisms, and scope conditions in the idiom your work demands.
When to trigger
- The empirics are strong but the "why" / mechanism is thin
- A reviewer said the paper is "atheoretical," "ad hoc," or "a model with no test"
- You need to state mechanisms, assumptions, and scope conditions explicitly
- Formal modeling: deciding what to model, what to assume, and what the model buys
Build the argument (by mode of work)
Empirical paper with a theory
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Concept — define key constructs precisely; distinguish from neighbors and from how they will be
measured (hand off to
ajps-data-analysis for validation).
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Mechanism — the causal story: who acts, why, under what incentives/constraints.
-
Hypotheses — state the directional, testable expectations before the results, and what
pattern would disconfirm them. These become the tests in
ajps-research-design.
-
Scope conditions — where the argument holds and where it does not.
Formal / game-theoretic paper
- State the substantive puzzle the model addresses before the setup.
- Keep assumptions transparent and motivated; flag which results depend on which assumptions.
- Translate equilibrium predictions into comparative statics a reader can test or recognize.
- Make the empirical test follow from the model, and distinguish predictions unique to your model
from those shared with rivals.
Measurement / methodology paper (Research Note territory)
- Define the quantity the new measure/method recovers and the bias in existing approaches.
- State the conditions under which it outperforms incumbents, with a validation plan.
The "testability" gate (AJPS-specific)
Before proceeding, write the sentence: "If the theory is right, we should observe ___; if a rival is
right, we should observe ___ instead." If you cannot fill both blanks with something the data can
distinguish, the theory is not yet ready for an AJPS empirical confrontation.
Anti-patterns
- HARKing — fitting hypotheses to results after the fact; state theory before tests, preregister where possible
- A model whose assumptions are reverse-engineered to deliver the desired prediction
- Mechanisms named but never made observable or testable
- Universal claims with no scope conditions
- Burying the argument under the empirics — the contribution must be stated plainly up front
Operating pass for American Journal of Political Science
Run this as a concrete capability pass. First lock the political theory, design leverage, measurement validity, and scope condition; then test whether the manuscript addresses political-science reviewers who expect tight theory, transparent design, and a contribution that travels across political settings.
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Primary move: Return a claim-evidence-risk ledger; every recommendation must point to a manuscript location or missing artifact.
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Decision ledger: return
claim / evidence / blocker / next edit rows so the next pass can patch the manuscript directly.
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Sibling comparison: compare against APSR for field-wide breadth, Journal of Politics for broader political-science audience, Political Analysis for methods-first work; if the neighboring outlet has the stronger audience claim, recommend re-routing before polishing.
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Verification floor: before submission-ready advice, re-open
resources/official-source-map.md for volatile rules and name the one unresolved fact that could change the recommendation.
Output format
【Core claim】one sentence
【Mechanism】the causal/logical story
【Hypotheses】directional, stated before results
【Assumptions】(formal) the load-bearing ones
【Disconfirming pattern】what would falsify it
【Scope conditions】where it holds / fails
【Next】ajps-research-design
Supplementary resources
