Responding to Self-Consciousness: An Examination of Everyday and Dream Episodes
Bibliographic record
Abstract
Eighty-nine students from the University of Toronto completed a 20-item self-consciousness questionnaire. They also provided a written account of a self-conscious (e.g., embarrassing) episode from everyday life as well as a self-conscious episode from a dream. Factor analysis of the questionnaire responses revealed 6 factors, with public and private self-consciousness emerging as the dominant factors. The everyday and dream episodes were examined qualitatively and 8 categories were derived the frequency of which could be numerically assessed in each protocol and factor analyzed. For the everyday life episodes, 5 factors revealed concerns of undergraduates related to academic performance versus physical appearance, social anxiety, and athletic performance, as well as 2 coping strategies. Three factors emerged from the dream episodes with the first revealing how students imagined coping with negative bodily arousal in order to assert themselves in academic situations. The second factor showed how students imagined withdrawing from social situations so as to avoid negative emotions associated with self-consciousness related to appearance. Meaningful correlations were found relating factors from the everyday episodes with the self-consciousness questionnaire as well as the dream episodes. These findings demonstrate complementary relations between quantitative and qualitative (i.e., narrative-based) assessments of self-consciousness.
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".