Falling off the Agenda: Pandemic Planning and How Windsor-Essex Performs in Provincial Comparison
Bibliographic record
Abstract
In the fall of 2020, we undertook Mitacs funded research with the intention of creating a knowledge base of best practices for the design and implementation of emergency plans for local government. To do so we employed a mixed methods approach examining municipal emergency planning documents in place prior to the Covid-19 pandemic to assesses their effectiveness in dealing with the specific Covid-19 situation. Ultimately, we ended up assessing Ontario's governmental institutions and organizations and noting where their failings and successes were in comparison to the prior SARS Commission pandemic planning indicators. While we went into our research focussed on the municipal level of public health organization, we couldn't escape how interconnected our Canadian political and administrative systems were with authority distributed through federal, provincial and local levels of influence. Primary focus will revolve around Ontario's public health organization acknowledging that there are currently thirty-four public health units in Ontario: twenty of which are independent of local municipal government; seven which are regional health departments; and seven which are health units tied in to single-tier or other municipal administration. This presentation will summarize our basic research findings and employ a local lens to showcase how Windsor-Essex preparedness and current pandemic response measures up respective to the provincial average.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".