Bibliographic record
Abstract
On October 28, 2020, Canada’s Chief Medical Officer of Health, Dr Theresa Tam, stated that the pandemic was exposing existing inequalities in Canada. In a television address, she said that: ‘The impacts of COVID- 19 in this country have been worsened by systems that stigmatize populations through racism, ageism, sexism, and others, who have been marginalized through structural or social factors such as homelessness … Differences [in infection rates] are not random, but all along the lines of populations that have historically experienced health and social inequities … The impacts have been worse for some groups such as seniors, workers who provide essential services, such as those in health care or agriculture, racialized populations, people living with disabilities, and women. The virus didn’t create new inequities in our society; it exposed them and underscored the impact of our social policies on our health status’. While this may have been a revelation for some people, for the contributors to this volume, and the others in this series, such a direct and unequivocal statement was not surprising. Such insights are also nothing new to those with lived experiences of these inequalities and injustices. Throughout the four volumes, one of our aims has been to include chapters that amplify these voices by meaningfully, respectfully, and ethically engaging with marginalized communities in order to center their experiences within planning, policy, and political debates about the impact of the pandemic and, importantly, how to respond to it. The findings, analysis, and reflection found throughout this series of books must be a reminder to planners and policy makers that the divisions, inequities, and injustices rendered visible during the COVID- 19 pandemic long predate the virus.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.267 | 0.096 |
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".