Backwards counterfactuals and the closest possible world
Bibliographic record
Abstract
One source of complexity in counterfactual reasoning is the order in which events are presented within the conditional. Counterfactuals with a backwards order of events (aka ‘backtracking’ counterfactuals) involve reasoning backward: from the consequent to the antecedent. We extend on prior experimental work (e.g., Rips 2010), and consider the possibilities adults reason over when they backtrack. We find that adults’ reasoning strategies tend to be inconsistent when responding to backtracking questions. In scenarios involving a single causal variable, participants do not generally allow for extraneous changes from the actual world. Furthermore, when reasoning forward along a causal chain, participants do not allow for extraneous changes. However, in backtracking scenarios involving multiple causal variables, participants are at chance in choosing worlds with extraneous changes. We provide novel evidence for the changes allowed from the actual world when backtracking, with mixed support for theoretical claims such as Minimal Networks Theory.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".