Bibliographic record
Abstract
Abstract This chapter argues that the common claim that the ascription of ecological functions to organisms in functional ecology raises issues about levels of natural selection is ill-founded. This claim, I maintain, mistakenly assumes that the function concept as understood in functional ecology aligns with the selected effect theory of function advocated by many philosophers of biology (sometimes called “The Standard Line” on functions). After exploring the implications of Wilson and Sober’s defence of multilevel selection for the prospects of defending a selected effect account of ecological functions, I identify three main ways in which functional ecology’s understanding of the function concept diverges from the selected effect theory. Specifically, I argue (1) that functional ecology conceives ecological functions ascontext-basedrather thanhistory-basedproperties of organisms; (2) that it attributes to the ecological function concept the aim of explaining ecosystem processes rather than that of explaining the presence of organisms within ecosystems; and (3) that it conceives the ecological functions of organisms asuseandservicefunctions rather thandesignfunctions. I then discuss the extent to which the recently proposed causal role and organizational accounts of ecological functions better accord with the purposes for which the function concept is used in functional ecology.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".