MétaCan
Menu
Back to cohort
Record W4230900826 · doi:10.32920/ryerson.14640531.v1

Les effets des programmes d’été de littératie: Les théories sur les opportunités d’apprentissage et les élèves « non traditionnels » dans les écoles de langue française en Ontario

2021· preprint· en· W4230900826 on OpenAlexaboutno aff
Scott Davies, Janice Aurini, Emily Milne, Johanne Jean‐Pierre

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsDisadvantagedLiteracyHumanitiesPsychological interventionPsychologyPolitical sciencePedagogyArt

Abstract

fetched live from OpenAlex

According to studies from the United States and English Canada, student achievement gaps grow over the summer months when children are not attending school, but summer literacy interventions can reduce those gaps. This paper presents data from a quasi-experiment conducted in eight Ontario French language school boards in 2010, 2011 and 2012 for 682 children in grades 1-3. Growth in literacy test scores between June and September are compared for 361 attendees of summer literacy programs and 321 control students. Summer program recruits initially had lower prior literacy scores and grades, and tended to hail from relatively disadvantaged social backgrounds. Yet, summer programs narrowed those pre-existing gaps. Effect sizes from a variety of regression and propensity score matching models ranged from .32 to .58, which is quite sizeable by the standards of elementary school interventions and summer programs. Effects were stronger among students whose parents reported not speaking French exclusively at home. Our paper considers learning opportunity theory in light of the “non-traditional” student in Ontario French language schools.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient 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 score0.250
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.056
GPT teacher head0.310
Teacher spread0.255 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicSchool Choice and PerformanceFrench-language works237,207