Bibliographic record
Abstract
The role of reciprocal causation in the Extended Evolutionary Synthesis (EES) is controversial. On the one hand, reciprocal causation is considered a key innovation of EES, thereby justifying EES’s existence. On the other, EES skeptics argue that Standard Evolutionary Theory (SET) already sufficiently accounts for reciprocal causation. Two arguments support criticisms directed at the role of reciprocal causation in EES. First, the misrepresentation argument claims that EES proponents mischaracterize causal notions in SET. Second, the empirical argument provides concrete examples of how reciprocal causation is well-acknowledged in SET’s traditional evolutionary explanations. Neither argument has generated constructive debate surrounding the role of reciprocal causation in evolutionary explanations. In this paper, I propose a third argument—the scope argument—which analyzes reciprocal causation in terms of timescales and grain of explanations. The scope argument reframes 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".