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Record W4285033944 · doi:10.22215/etd/2022-15059

Curiosity and Interesting Conversations as Factors that Reduce Relational Boredom in Intimate Relationships

2022· dissertation· en· W4285033944 on OpenAlex
Marcus Hebert

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

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicPsychological and Educational Research Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsBoredomCuriosityPsychologySocial psychology

Abstract

fetched live from OpenAlex

This thesis examined the associations between curiosity, interesting conversations with intimate partners, and relational boredom. I hypothesized that high (vs low) socially-curious people have more frequent interesting conversations and use more interest-related self-regulatory strategies and that this, in turn, is associated with less boredom. Two online studies were conducted with samples of undergraduate students in dating relationships. In Study 1 (N = 137), people high (vs low) in social curiosity more frequently had interesting conversations, and interesting conversations were associated with less boredom. In Study 2 (N = 140), people high (vs low) in social curiosity used more interest-related self-regulatory strategies, and these strategies were associated with less boredom. In Study 2, curiosity subtypes (joyous exploration and thrillseeking) were also associated with using interest-related self-regulatory strategies. The results imply that curious people have skills to create experiences with their partners, such as interesting dinner conversations, that strengthen their relationships.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0180.000

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 head0.245
GPT teacher head0.454
Teacher spread0.208 · 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