French Nonprofit Organizations Facing COVID-19 and Lockdown: Maintaining a Sociopolitical Role in Spite of the Crisis of Resource Dependency
Bibliographic record
Abstract
COVID-19 became a global health crisis affecting all collective spheres. French nonprofit organizations (NPOs) are trying to participate in the crisis response, but they are suffering from the consequences of the crisis and a structural lack of re- sources. The aim of this article is to understand how they reacted to the crisis and how they coped with the associated lack. It is based on an extensive survey of French NPOs during the first lockdown. The results show that NPOs consider- ably reduced their activity while trying to maintain social links. Despite the reorganization of working arrangements during COVID-19, the economic impact of the crisis was very strong. RÉSUMÉ La crise sanitaire du Covid-19 devient une crise globale qui touche toutes les sphères collectives. Les associations fran- çaises tentent de participer à la réponse à la crise mais, en dépit de cet engagement, elles subissent de plein fouet les conséquences de la crise et souffrent du manque de ressources qui est déjà structurel chez elles. L’objectif est de com- prendre comment elles ont réagi à la crise en composant avec ce manque. L’article s’appuie sur une enquête de grande ampleur auprès des associations durant le premier confinement. Les résultats montrent que les associations ont consi- dérablement réduit leur activité, tout en essayant de maintenir des liens sociaux si possible. L’impact économique est cependant très fort, malgré la réorganisation des modalités de travail.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".