Bibliographic record
Abstract
Nonprofit organizations in Canada were significantly impacted by COVID-19, including lost revenue and needing to adjustthe program delivery. The lack of technology capacity in the nonprofit sector is a key barrier for many nonprofit organizations to adapt to delivering programs online. Momentum, a Calgary-based nonprofit organization, experienced both financial and programmatic challenges due to COVID-19. Momentum pivoted program delivery to provide supports during the COVID-19 lockdown and developed innovative approaches to online programming. Since the start of the COVID-19 pandemic in Canada, Momentum was able to rapidly develop its capacity to use technology for online programming with the support of critical new funding. Many nonprofits will have to transform their business models to not only survive but thrive in the post-COVID world.Les organismes à but non lucratif (OBNL) au Canada ont été fortement touchées dans le contexte de la pandémie de laCOVID-19, notamment à cause d'une perte de revenus et de la nécessité de se réajuster afin de prêter des services enligne. Le manque de capacités technologiques dans le secteur à but non lucratif est un obstacle majeur à l'adaptation denombreux OBNL à la prestation de services en ligne. Momentum, un OBNL basé à Calgary, a connu des difficultésfinancières et de planification en raison de la COVID-19. Par contre, l'organisme a su adapter son offre de services pourfournir un soutien pendant le confinement et a développé des approches innovantes pour la prestation de services enligne. Depuis le début de la pandémie au Canada, Momentum a développé rapidement sa capacité à utiliser la technologiepour offrir des services en ligne grâce à des nouvelles sources de financement qui ont été essentielles pour cetteadaptation. De nombreux OBNL devront transformer leur modèle d'entreprise pour non seulement survivre, mais aussiprospérer dans un monde post-COVID.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.043 | 0.015 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".