Indispensable Insight: Children's Perspectives on Factors and Mechanisms That Promote Educational Resilience.
Bibliographic record
Abstract
In order to foster educational resilience in children who face adversity, adults need a clear grasp of which factors are most relevant and motivating for these children. This study asked 50 children (ages eight to 12) who face serious life difficulties to share their perspectives on which factors support academic performance and how those factors operate in their lives. Participants identified eight factors (intelligence, feelings, behaviours, home environment, family assistance, school support, community connections, organized programs) that improve academic performance and described three mechanisms (facilitating work, increasing understanding, preventing negative behaviour) by which the factors function. Implications for practice and further research are discussed. Resume Afin d’encourager la resilience chez les enfants faisant face a des contextes d’adversite, les adultes doivent clairement saisir quels facteurs sont les plus pertinents et motivants pour ces enfants. Nous avons demande a cinquante enfants entre huit et douze ans faisant face a des serieuses difficultes de nous indiquer quels facteurs soutenaient la performance academique, et comment ces facteurs se deployaient dans leur vie. Les participants ont identifie huit facteurs (intelligence, sentiments, comportements, environnement familial, soutien familial, soutien scolaire, contacts dans la communaute, programmes structures) qui amelioraient leurs performances academiques, et ont decrit trois mecanismes (faciliter le travail, augmenter la comprehension, prevenir les comportements negatifs) par lesquels les facteurs entraient en action. Nous abordons ensuite les implications de ces donnees pour la pratique et la recherche.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".