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Record W2760929212 · doi:10.1177/0272431617735651

Grade Retention at the Transition to Secondary School: Using Propensity Score Matching to Identify Consequences on Psychosocial Adjustment

2017· article· en· W2760929212 on OpenAlexaff
Cécile Mathys, Marie‐Hélène Véronneau, Aurélie Lecocq

Bibliographic record

VenueThe Journal of Early Adolescence · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversity of OttawaUniversité du Québec à Montréal
Fundersnot available
KeywordsPsychosocialPropensity score matchingGrade retentionPsychologyCompetence (human resources)UnivariateDevelopmental psychologyClinical psychologyMedicineAcademic achievementMultivariate statisticsSocial psychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

This study tested whether grade retention at the transition into secondary school had a significant impact on adolescent psychosocial adjustment. A quasi-experimental design was used in which propensity score matching was implemented. Univariate ANCOVAs were subsequently run on a subsample of 181 students enrolled in one typical secondary school in the French-speaking region of Belgium ( M = 12.91 years, 55.8% girls). These analyses revealed that retained students experienced decreases in self-esteem, perceived parental support for competence and involvement in the relationships with their parents, and intrinsic and extrinsic motivation variables. Retained students also failed to show the decrease in delinquent and aggressive behaviors and social withdrawal that was observed in matched promoted students. In sum, grade retention appears to be detrimental to early-adolescence psychosocial adjustment. To decrease rates of grade retention among adolescents, change is needed in parents’, school staff’s, and policymakers’ preconceptions that the practice has overall positive outcomes.

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)0.016
metaresearch head score (Gemma)0.029
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 score0.016
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.105
GPT teacher head0.369
Teacher spread0.264 · 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

Citations26
Published2017
Admission routes1
Has abstractyes

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