A connected community response to COVID-19 in Toronto
Bibliographic record
Abstract
In this commentary, we describe initial learnings from a community-based research project that explored how the relational space between residents and formal institutions in six marginalised communities in Toronto, Ontario, Canada impacted grassroots responses to the health and psycho-social stresses that were created and amplified by the coronavirus disease 2019 (COVID-19) pandemic. Our research found that grassroots community leaders stepped up to fill the gaps left by Toronto's formal public health and emergency management systems and were essential for mitigating the psycho-social and socioeconomic impacts of the pandemic that exacerbated pre-existing inequities and systemic failures. We suggest that building community resilience in marginalised communities in Toronto can embody health promotion in action where community members, organisational, institutional and government players create the social infrastructure necessary to build on local assets and work together to promote health by strengthening community action, advocating for healthy public policy and creating supportive environments.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.029 | 0.014 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".