Generalized Selected Effects Functions and Ecology
Bibliographic record
Abstract
Expanding upon the “classical” selected effects (SE) theory of function, Justin Garson’s generalized selected effects (GSE) theory states that functions may derive not just from natural selection, but from a broader range of selection processes involving differential retention as well as reproduction. In this paper, we consider whether the GSE theory’s broadened range of selection processes makes it more promisingly applicable to ecology than the classical SE theory. We argue that, although a GSE account of ecological role functions would evade some of the reasons that the SE theory of function has been considered poorly applicable to ecology, alternative theories of function, and notably the persistence enhancing propensity (PEP) account of ecological role functions, remain more appropriate partly given the purpose for which the concept of role function is used in ecology. The GSE theory’s backward-looking character meshes poorly with the fact that, in ecology, the concept of role function is used mainly to explain how ecosystems are able to achieve their processes reliably rather than to explain the presence of certain ecological items (e.g., organisms, populations, species) within them. We argue this in part by comparing the implications of a GSE account of ecological role functions with those of the PEP account with respect to three types of cases: dormant species, sink populations, and abiotic items. We draw out implications of our discussion for Garson’s take on function pluralism and his overall defense of the GSE 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".