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Record W4309545984 · doi:10.53967/cje-rce.5555

Academic Resilience During the COVID-19 Pandemic: A Triarchic Analysis of Education Policy Developments across Canada

2022· article· en· W4309545984 on OpenAlexafffundvenueabout
Louis Volante, Camila Lara, Don A. Klinger, Melissa Siegel

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2022
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPandemicScope (computer science)Resilience (materials science)Coronavirus disease 2019 (COVID-19)Content analysisPsychological resiliencePolitical scienceMental healthSociologyPsychologySocial scienceMedicineSocial psychology

Abstract

fetched live from OpenAlex

This study employed a qualitative content analysis of provincial policy documents, following deductive methods, to examine academic resilience and education policy developments across Canada during the COVID-19 pandemic. More specifically, the study explicates the nature and scope of provincial policy responses to the global pandemic that address academic issues, physical health and well-being, and mental health issues for K–12 students. The pan-Canadian analysis revealed a total of 62 documents were issued between January 2020 and December 2021 that addressed one or more of the triarchic dimensions of academic resilience. The findings suggested greater attention was devoted to academic issues and there was a general lack of policy differentiation in terms of how specific resources and supports were to be directed within provincial educational jurisdictions.

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)0.009
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.813

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.012
Science and technology studies0.0200.008
Scholarly communication0.0070.002
Open science0.0020.005
Research integrity0.0010.003
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 head0.075
GPT teacher head0.423
Teacher spread0.348 · 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 designNot applicable
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

Citations6
Published2022
Admission routes4
Has abstractyes

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