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Record W7096013504

and Student–Teacher Relationships Matter for Academic Achievement? A Multilevel Analysis

2016· article· en· W7096013504 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Research and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsMultilevel modelSocial connectednessMultilevel modellingAffect (linguistics)School climateRelation (database)Academic achievementSocial relation
DOInot available

Abstract

fetched live from OpenAlex

In extending our understanding of how the social climate of schools can affect academic outcomes, this study examined the relationship between school bullying, student– teacher (S-T) connectedness, and academic performance. Using data collected in Canada as part of a larger international study conducted by the Organisation for Economic Co-operation and Development, participants included 27,217 students aged 15 years and 1,087 school principals. Results of multilevel analyses revealed that math achievement was negatively related to school bullying and positively related to S-T connectedness. For boys, there was a significant interaction between bullying and S-T connectedness, suggesting a buffering effect of S-T connectedness on the relationship between school bullying and math achievement. Similar results were evident for reading achievement. Résumé Pour élargir notre compréhension de comment le climat social de l’école peut avoir un impact sur la réussite scolaire, cette étude examine la relation entre l’intimidation à l’école, la relation élève-enseignant et le rendement scolaire. Les données utilisées ont été recueillies au Canada pour une étude internationale plus large menée par l’Organisation de coopération et de développement économiques, les participants

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.100
GPT teacher head0.345
Teacher spread0.244 · 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 teacher head, not a consensus.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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