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Record W3125112999 · doi:10.1503/jpn.210004

Why did the kitten cross the road? A meditation on positive versus negative reinforcement in addiction

2021· editorial· en· W3125112999 on OpenAlexaffvenue
Marco Leyton

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2021
Typeeditorial
Languageen
FieldNeuroscience
TopicNeurotransmitter Receptor Influence on Behavior
Canadian institutionsUniversité de MontréalConcordia UniversityMcGill UniversityResearch Unit on Children's Psychosocial MaladjustmentMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsKittenMeditationPsychologyReinforcementAddictionPsychotherapistPsychoanalysisDevelopmental psychologyNeuroscienceSocial psychologyMedicinePhilosophyInternal medicineTheology

Abstract

fetched live from OpenAlex

Why did the kitten cross the road? One possibility is that there is something better on the other side (e.g., warm sun or sun-paired cue) ([Box 1a][1]). A second possibility is that there is something lacking or depleting on the first side (e.g., temperature sapping cold). Distinguishing between

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0030.001
Research integrity0.0120.019
Insufficient payload (model declined to judge)0.0060.003

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 head0.024
GPT teacher head0.323
Teacher spread0.299 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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

Citations3
Published2021
Admission routes2
Has abstractyes

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