Bibliographic record
Abstract
The goal of this thesis is to examine whether perceptions of too much self-expansion affects shared leisure experiences.I hypothesize that too much relational self-expansion will be associated with a tendency to engage in less exciting and more familiar shared activities.In Study 1, relational self-expansion was manipulated (i.e., too much, just enough, not enough) in hypothetical couples and participants rated the types of dates (i.e., exciting or familiar) the couple should engage in.In Study 2, participants reported their own relational self-expansion and rated the excitement and familiarity of the date they planned.As predicted, when participants read about someone experiencing too much self-expansion in their relationship, they selected dates for the couple that were more exciting and less familiar (Study 1).However, these findings did not extend to people's own relationships (Study 2).These findings highlight the importance of self-expansion perceptions and integration for relationship maintenance behaviour.throughout this process whilst still allowing me to grow my research skills.It can be a difficult role to be the one to provide feedback on someone's work, but she has mastered the ability to provide valuable feedback in a kind manner.She is encouraging, passionate and knowledgeable.Her knowledge, passion and encouragement were essential to my thesis.Thank you for your opinions, kindness, and encouragement throughout my thesis.I would also like to
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".