Bibliographic record
Abstract
Previous research has indicated that increasing self-esteem before an exam actually hinders ones performance (Forsyth et al., 2007). Furthermore, Woodman, Akehurst, Hardy & Beattie (2010) have indicated that when one’s self-confidence is decreased, on-task effort is increased. The purpose of the current study was to determine if priming an individual’s memory of an academic success or failure would alter ones confidence and motivation to complete a task. Specifically, it was assumed that individual’s who are primed of a past academic failure would have lower confidence going into a task, but show greater motivation in completing a task. Conversely, those who are primed of a past academic success will have high confidence going into a task but low motivation in completing the task. The task at hand was completing as many easy or hard anagrams as one liked. Thirty-two participants attending the University of Western Ontario and its affiliates participated in thestudy. When analyzing participants’ motivation, the findings although not significant, did indicate a trend in the predicted direction. Whereby, participants spent more time and completed more anagrams in the failure/hard condition than in the success/hard condition. However, the results indicated no significant difference in participants’ self-confidence ratings between primed conditions, thus refuting the original hypothesis that confidence would be lower when participants were asked to think of a past academic failure in comparison to when participants were asked to think of a past academic success. The implications of these findings are discussed.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".