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Record W4390105003 · doi:10.33524/cjar.v23i2.610

Rural Remote Learning in Manitoba During COVID-19: Opportunities and Challenges of Action Research

2023· article· en· W4390105003 on OpenAlexaffvenueabout
Cathryn Smith, Gustavo Moura

Bibliographic record

VenueThe Canadian Journal of Action Research · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsBrandon University
Fundersnot available
KeywordsGeneral partnershipAction researchCoronavirus disease 2019 (COVID-19)Context (archaeology)PandemicAction (physics)Participatory action researchSociology2019-20 coronavirus outbreakPublic relationsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Action learningPedagogyPolitical scienceGeographyMedicineTeaching methodCooperative learning

Abstract

fetched live from OpenAlex

In September of 2020, seven school divisions in Western Manitoba developed a remote learning program to support medically fragile families whose children could not return to classrooms. The coalition of these school divisions, known as the Westman Consortia Partnership (WCP), needed to investigate what beliefs, practices, and strategies were critical to this new rural remote learning program, hence the collaboration with researchers to answer that question. From action research perspectives, this paper unpacks opportunities and challenges researchers faced in pre-, peri-, and post- research contexts during the COVID-19 pandemic. The paper explores action research aspects that were both followed and disrupted given the social, cultural, and historical context of the participants in the study.

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)0.015
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.513

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0390.024
Scholarly communication0.0080.002
Open science0.0040.014
Research integrity0.0040.008
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 head0.723
GPT teacher head0.560
Teacher spread0.163 · 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 designQualitative
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
Published2023
Admission routes3
Has abstractyes

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