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Record W4403325871 · doi:10.3998/ptpbio.5657

Generalized Selected Effects Functions and Ecology

2024· article· en· W4403325871 on OpenAlexaff

Bibliographic record

VenuePhilosophy Theory and Practice in Biology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntellectual Property Law
Canadian institutionsUniversité de MontréalCollège Lionel Groulx
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.624
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.351
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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