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Record W6961408916 · doi:10.15139/s3/i7zivw

Partner Regulation and Gratitude - Cross-Sectional, 2019

2023· dataset· en· W6961408916 on OpenAlexaff

Bibliographic record

VenueUNC Dataverse · 2023
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGratitudeRomanceAttributionInterpersonal relationshipSocioemotional selectivity theorySelf-disclosure

Abstract

fetched live from OpenAlex

A total of 486 participants (58.8% women, 40.5% men, 0.6% non-binary) in a romantic relationship were recruited from the online Prolific Academic platform. To be eligible for the study, participants were required to be at least 18 years old and to have been involved in their romantic relationship for at least one year. The average relationship length was 8.69 years (SD = 8.27) and the majority of participants (75.5%) were in committed and unmarried relationships, 23.2% married, and 1.4% did not report their relationship type. Participants’ average age was 32.16 years (SD = 10.15, range = 18-75), and they identified as 85.0% heterosexual, 9.3% bisexual, 1.6% gay, 1.6% pansexual, 1% lesbian, 0.4% asexual, 0.2% other, (0.8% preferred not to say), and as 75.7% White, 8.8% Asian/Pacific Islander, 7.2% Hispanic or Latinx, 2.5% Black or African American, 0.6% Indigenous, and 5.1% as another racial/ethnic identity. Participants completed measures about a change their romantic partner asked them to make, including their motivations for making efforts toward these changes, attributions for the change request, how their partner requests change, and the gratitude they receive from their partner for their efforts.

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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.373
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.010

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 head0.036
GPT teacher head0.272
Teacher spread0.236 · 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
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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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