Master Narratives, Expectations of Change, and Their Effect on Temporal Appraisals
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.
Bibliographic record
Abstract
People hold narrative expectations for how humans generally change over the course of their lives. In some areas, people expect growth (e.g., wisdom), while in others, people expect stability (e.g., extroversion). However, do people apply those same expectations to the self? In five studies (total N = 1,372), participants rated selves as improving modestly over time in domains where stability should be expected (e.g., extroversion, quick-wittedness). Reported improvement was significantly larger in domains where growth should be expected (e.g., wisdom, rationality) than domains where stability should be expected. Further, in domains where growth should be expected participants reported improvement for selves and others. However, in domains where stability should be expected, participants reported improvement for selves but not others. Hence, participants used narrative expectations to inform projections of change. We discuss implications for future temporal self-appraisal research, heterogeneity of effect sizes in self-appraisal research, and between-culture differences in narratives.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it