Bibliographic record
Abstract
Most people have struggled with studying at some point in their lives. A tactic that has been used to alleviate this issue is to reward oneself for studying. MacEwan University students (N = 353) were asked to complete an online survey that included the following questions: (1) To what extent do you reward yourself in some way when you complete a study session? (2) To what extent do you deliberately plan some type of reward following a study session? (3) To what extent do you reward yourself following a study session, but only if you successfully complete the study session as planned? Students’ ratings on these items were correlated with their ratings of whether they also successfully completed their study plans. The results showed that while all the self-reward strategies were significantly correlated with study plan completion, the regression analysis indicated that rewarding oneself only after the study session was successfully completed (contingent self-reward) was most predictive of study plan completion. These results have implications for the kinds of advice offered to students to improve their study behaviour. Limitations of these findings include the correlational nature of the study and the use of self-report measures only. Faculty Mentor: Russ Powell Department: Psychology (Honours)
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 | 0.018 |
| 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 | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".