Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it