Are academic discounting and devaluing double-edged swords? Their relations to global self-esteem, achievement goals, and performance among stigmatized students.
Bibliographic record
Abstract
Often taken for granted, the coexistence of benefits and costs of discounting and devaluing has never been tested. Yet, not only are there inconsistent findings about the relations between these processes and global self-esteem, but little is known about their relations to motivation and performance. Here we simultaneously examined how academic discounting and devaluing relate to global self-esteem, achievement goals, and grades. Conducted among a diverse sample of stigmatized secondary-school students, the present study showed that discounting was positively related to global self-esteem (even more for students with lower academic self-esteem) and unrelated to achievement goals and grades. Devaluing was unrelated to global self-esteem, negatively related to all achievement goals, and associated with lower grades through mastery-approach goals. These findings suggest that discounting is a true self-protective strategy, buffering self-evaluation without decreasing achievement motivation. On the contrary, devaluing appears as a maladaptive strategy, with no benefit at all for self-evaluation and major costs for achievement motivation and grades.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".