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Record W4389092834 · doi:10.5947/jeod.2023.010

Social Resources and Cooperative Resilience: Findings from the Canadian Cooperative Sector During the COVID-19 Pandemic

2023· article· en· W4389092834 on OpenAlexaffabout
Anne-Marie Merrien, Josée Charbonneau, I. L. Jankovic, Sonja Novković, Fiona Duguid, Claude-André Guillotte, Étienne Fouquet

Bibliographic record

VenueJournal of Entrpreneurial and Organizational Diversity · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsSpinal Cord Injury BCSaint Mary's UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicResilience (materials science)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Political scienceVirologyMedicineOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The international cooperative movement boasts a strong historical legacy of resilience in times of crisis.Early research suggests that this legacy is continued in the cooperative experience during this most recent of global crises, the COVID-19 pandemic.This study examines the cooperative experience in the first year of the pandemic in Canada, including its impact and the ways in which cooperatives have responded and adapted to challenging conditions.Regardless of challenges they were facing, cooperatives experienced stability during the crisis.They mobilized solidarity measures to support their members, employees, local communities, and other cooperatives including offering financial assistance, in-kind products and services and adapting operations to support pandemic relief.While cooperatives benefited from government relief programs, membership and community support as well as their networked structures contributed to their resilience.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.427

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0280.008
Scholarly communication0.0060.002
Open science0.0020.008
Research integrity0.0010.003
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 head0.034
GPT teacher head0.277
Teacher spread0.243 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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