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Record W6905133764 · doi:10.15139/s3/k03yhd

Toronto Couples Study, 2017

2019· dataset· en· W6905133764 on OpenAlexaffabout

Bibliographic record

VenueUNC Dataverse · 2019
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSession (web analytics)InterviewOnline forumData collectionWork (physics)Protocol (science)

Abstract

fetched live from OpenAlex

This dataset was collected at the University of Toronto between 2015-2017. Romantic couples from the community participated in a four-part study. To be eligible, couples were required to be in a relationship for at least 3 years. A total of 111 couples completed the study. The four parts of the study included (1) completing a background questionnaire; (2) participating in a lab session in which they were connected to physiological equipment and held three conversations, alternating as speakers and listeners, on the topics of (a) a time when they felt distressed, (b) something they would like their partner to change and (c) something about their partner they feel grateful for; and completed questionnaires about these conversations; (3) completing a 14-day daily experience (“diary”) study; and (4) completing a follow-up questionnaire the day after the diary. In addition, a subset of these participants was rated by their work supervisors. Data have been collected for this study in full. The protocol reference number for ethics approval of this study is 31063.

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)0.002
metaresearch head score (Gemma)0.014
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.333
Threshold uncertainty score0.669

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.008
Science and technology studies0.0020.000
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0530.022

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 head0.046
GPT teacher head0.322
Teacher spread0.275 · 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
GenreDataset

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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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