Counterfactuals, mechanisms, and background beliefs in The Logic of Social Science
Bibliographic record
Abstract
Elegant in its architecture and sweeping in its ambition, James Mahoney's The Logic of Social Science (2021) addresses deep philosophy-ofscience foundations, set-theoretic methodology, and a suite of set-theoretic analytic tools. The text is exceedingly lucid and aided by visuals (Euler diagrams) that lend remarkable clarity to complex set-theoretic relations. Drawing on rich empirical examples, the book provides clear, actionable, innovative guidance on how to engage in case-level set-theoretic analysis of various forms, including counterfactual analysis, sequential analysis, and the analysis of critical events. Among the book's most enlightening features are the ways in which the it maps causal and inferential concepts native to other analytic frameworks into set theory. Perhaps the most remarkable of these translations is the book's set-theoretic rendering of Bayesian inference, in a chapter coauthored with Rodrigo Barrenechea. While I am entirely persuaded that Bayesianism assumes and requires a set-theoretic approach, as the authors claim, it is nonetheless striking to see how fully set-theory can represent a mode of inferential reasoning that we typically undertake in probabilistic terms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".