When to Use Counterfactuals in Causal Historiography: Methods for Semantics and Inference
Bibliographic record
Abstract
According to the interventionist framework of actual causality, causal claims in history are ultimately claims about special types of functional dependencies between variables, which consist not only of actual events but also of corresponding counterfactual states of affairs. Instead of advocating the methodological use of counterfactuals tout court , we propose specific circumstances in historical writing where counterfactual reasoning comes in most handy. At the level of semantics, that is, the specification of the variables and their possible values, an explicit specification of the latent contrast classes becomes particularly useful in situations where one may be prompted to take an event that is pre-empted by the antecedent of interest as its proper causal contrast. At the level of inference, we argue that cases in which two or more antecedents appear to be playing a similar role tend to fumble our pretheoretical intuition about cause and propose a sequence of counterfactual tests based on actual examples from causal historiography.
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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 | 0.081 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.016 | 0.045 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".