Bibliographic record
Abstract
Here we endorse Hull's replicator/interactor framework as providing the overarching understanding sought by MacCord and Maienschein.We suggest that difficulties in seeing the regeneration of limbs by salamanders and of forest ecosystems after fires as similar evolutionary processes can be overcome in this framework.In generalizing Dawkins's "selfish gene" perspective, Hull defined natural selection as "a process in which the differential extinction and proliferation of interactors causes the differential perpetuation of the replicators that produced them".Although genes and bacteria are simultaneously both replicators and interactors, communities and ecosystems are generally only interactors.As reproducers, members of sexual species are intermediate.Within such species, organisms are indeed the interactors whose "differential extinction and proliferation" causes the "differential perpetuation" of replicators (genes).But sexually-reproducing organisms do not individually replicate, persist, or recur as interactors, and in consequence it is only those genes causing an interactor differential that are specifically perpetuated in the long run.Higher-level interactors (multi-species communities and ecosystems) may seldom if ever reproduce, but their recurrence does enable the "differential perpetuation" of replicators specifically responsible for their "differential extinction and proliferation".In offering answers to the question "What are the replicators specifically responsible for the differential extinction and proliferation of such higher-level entities?"we hope to unify adaptive regeneration across scales, from organisms to ecosystems.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.796 | 0.696 |
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".