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Surviving a Pandemic: The Adaptability and Sustainability of Nonprofit Organizations through COVID-19

2021· article· fr· W3210036065 on OpenAlexaffvenueabout
Salewa Olawoye-Mann

Bibliographic record

VenueCanadian journal of nonprofit and social economy research · 2021
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsYork University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicPolitical scienceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakHumanitiesRevenueBusinessMedicineArtVirologyFinanceOutbreak

Abstract

fetched live from OpenAlex

Like many other organizations in Canada and globally, nonprofit organizations have not been insulated from the negative effects of the COVID-19 pandemic. It has affected Canadian nonprofit organizations in numerous ways. This ranges from the effects of COVID-19 on the health of workers and clients to its effect on revenue. As predominantly essential service providers, nonprofit organizations have to find ways to continue operations during the COVID-19 pandemic to ensure that no one is left to fall through the cracks in an uncertain economy. RÉSUMÉ Comme bien d’autres organismes au Canada et dans le monde, les organismes sans but lucratif (OSBL) n’ont pas été épargnés par la pandémie du COVID-19. En effet, pour les OSBL canadiens, celle-ci a eu des incidences dans divers secteurs, allant de la santé des clients et employés jusqu’au revenu. Les OSBL, comme ils sont à toutes fins pratiques des fournisseurs de services essentiels, doivent trouver le moyen de continuer à fonctionner pendant la pandémie afin de s’assurer que personne ne soit oublié dans un contexte économique incertain.

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.074
GPT teacher head0.345
Teacher spread0.271 · 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

Citations6
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueCanadian journal of nonprofit and social economy researchSame topicSupply Chain Resilience and Risk ManagementFrench-language works237,207