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Record W2769940731

Categorization: Causes and Consequences

2017· article· en· W2769940731 on OpenAlexaboutno aff
Catherine Plowright

Bibliographic record

VenueeScholarship (California Digital Library) · 2017
Typearticle
Languageen
FieldPsychology
TopicAcademic and Historical Perspectives in Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsCategorizationPsychologyCognitive psychologyComputer scienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

The Canadian Society for Brain, Behaviour and Cognitive Science (CSBBCS) is the face of behavioral neuroscience in Canada.With a broad outlook on experimental psychology, it appeals to students and their supervisors from across the country and beyond.While we all have much in common, there are also some obstacles to communication, not the least of which are the human/other-animal divide, and the various methods and theoretical perspectives we hold.An ecological approach connects seemingly disparate areas by opening our eyes to both causal and functional questions: we have not completely understood behavior unless we grasp its function, evolution, proximate causation and development-i.e., answers to all of Tinbergen's four questions "What is it for?How did it evolve?How does it work?How did it develop?".While none of this is news to readers of the International Journal of Comparative Psychology (IJCP), it is still not common currency in Psychology today.This is all changing.Indeed, CSBBCS awarded the Donald O. Hebb distinguished contribution award to Dr. Sara Shettleworth in 2012 for her lifetime contributions to "Cognition, Evolution and Behavior".The 2016 meeting of CSBBCS, organized by Dr. Charles Collin, included a symposium on "Categorization: Causes and Consequences" aimed at pursuing this approach and bringing together researchers who otherwise might not have had a chance to exchange ideas.Dr. Dani Brunner, editorin-chief of IJCP at the time, had suggested that a symposium might be paired with a call for papers and turned into a special issue.Here it is.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.849
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.031
GPT teacher head0.297
Teacher spread0.266 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2017
Admission routes1
Has abstractyes

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