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Record W2002933348 · doi:10.1080/02643294.2012.671766

Facing the challenge of variation in neuropsychological populations: Lessons from biology

2011· letter· en· W2002933348 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCognitive Neuropsychology · 2011
Typeletter
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsVariation (astronomy)Natural (archaeology)Population biologyPopulationNeuropsychologyState (computer science)EpistemologyPsychologyCognitive scienceCognitive psychologyBiologySociologyNeuroscienceCognitionComputer scienceDemographyPhilosophy

Abstract

fetched live from OpenAlex

Two ways of dealing with variation in biological populations are discussed. The first is referred to as the natural state model, an approach originated by Aristotle. The second emerged when biologists understood that lawful variation could be ascribed to an entire population of individuals as an organizational unit. This article establishes that the emphasis on single-case studies is driven by assumptions on the nature of variation that conform exactly to the natural state model. By contrast, the alternative case-series approach is consistent with population thinking in modern biology.

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 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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.140
GPT teacher head0.368
Teacher spread0.228 · 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