The Scope of Reciprocal Causation
Bibliographic record
Abstract
The role of reciprocal causation in Extended Evolutionary Synthesis (EES) is controversial. Proponents of EES argue that reciprocal causation is a key innovation, underpinning the necessity of EES. Conversely, critics of the EES maintain that Standard Evolutionary Theory (SET) adequately encompasses the concept of reciprocal causation, challenging the need for EES. This skepticism is rooted in two primary critiques. First, the mischaracterization of causal dynamics within SET by EES advocates leads to a misrepresentation of SET. Second, the oversight of how SET incorporates and acknowledges instances of reciprocal causation leads to claims about the empirical inaptness of SET. As a result, the debate has reached an impasse, with limited progress towards a constructive examination of reciprocal causation’s significance to evolutionary explanations. This paper introduces the scope argument, which examines reciprocal causation through timescales and grain of explanations. This approach revitalizes the debate in two ways. First, reframing the debate in terms of scope clarifies the role of reciprocal causation by allowing research programs to specify targets of explanation. Second, the elements of scope (timescales and grain) elucidate the epistemic advantage of reciprocal causation in the respective research programs in question.
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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 | 0.042 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".