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Record W2591649189 · doi:10.18584/iipj.2017.8.1.6

Aboriginal Language and School Outcomes: Investigating the Associations for Young Adults

2017· article· en· W2591649189 on OpenAlexaffvenue
Anne Guèvremont, Dafna Kohen

Bibliographic record

VenueInternational Indigenous Policy Journal · 2017
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of OttawaStatistics Canada
Fundersnot available
KeywordsAttendanceDuration (music)ConfoundingCohortPsychologyDevelopmental psychologyDemographyFirst languageMedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

Being taught an Aboriginal language at school has generally been associated with positive school outcomes for Aboriginal children but not adults. This study attempted to understand this discordance by examining three possible explanations: (a) confounding variables, (b) a cohort effect, and (c) differences in the timing and duration of Aboriginal language instruction. Confounding variables (school attendance on reserve, parental education, and family residential school attendance) and duration of Aboriginal language instruction (six of more grades) were important contributors; whereas the presence of a cohort effect and the timing of Aboriginal language instruction were not found to be significant. Future studies of Aboriginal language instruction should consider family educational experiences, location of schooling, and the duration of Aboriginal language instruction.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.854
Threshold uncertainty score0.290

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.412
Teacher spread0.386 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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