MétaCan
Menu
Back to cohort
Record W2143161907 · doi:10.7202/1003574ar

Are Strengths the Solution? An Exploration of the Relationships among Teacher-rated Strengths, Classroom Behaviour, and Academic Achievement of Young Students

2011· article· en· W2143161907 on OpenAlexaffvenue
Jessica Whitley, Edward P. Rawana, Melissa Pye, Keith Brownlee

Bibliographic record

VenueMcGill Journal of Education / Revue des sciences de l éducation de McGill · 2011
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsLakehead UniversityUniversity of Ottawa
Fundersnot available
KeywordsPsychologyProsocial behaviorAcademic achievementStrengths and weaknessesStrengths and Difficulties QuestionnaireDevelopmental psychologyMathematics educationSample (material)Social psychology

Abstract

fetched live from OpenAlex

Strength-based approaches are being increasingly validated for use in clinical settings with children and youth. However, the role that strengths play in educational settings with typically-achieving students has yet to be examined. The present study explored the relationship among strengths, classroom behaviour, and academic achievement for a sample of 54 students in Grades 1 and 2. Results showed that teachers rated female students as having more strengths than male students. For both sexes, academic achievement was most highly related to strengths in School Functioning and prosocial behaviour. Strengths in Peer Relationships were significantly related to achievement only for male students. Discussion of these findings, as well as implications for practice are presented.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.545
GPT teacher head0.484
Teacher spread0.061 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueMcGill Journal of Education / Revue des sciences de l éducation de McGillSame topicFamily and Disability Support ResearchFrench-language works237,207