Thinking ‘beyond’: critical reflections on race, racism, and the field of public health
Bibliographic record
Abstract
The COVID-19 pandemic has deeply impacted all aspects of life in Canada, revealing systemic racism as a foundational issue perpetuating health and social inequalities for racialized communities. In the field of public health, there is a growing recognition that addressing racism is crucial for achieving health equity and that anti-racist work is public health work. As guest editors of this special issue, we emphasize that in order to achieve this goal, the public health community needs to think in the ‘beyond’. To think in the beyond is to name, reflect on and subvert the epistemological, methodological, and practical conventions that dominate public health. The authors reflect on key considerations in this regard that account for historical contexts of epidemiology’s methods, tools and practice, biomedical constructs of race, relationships of racialized power in sustaining health inequalities, whiteness as an object of critical analysis, and notions of legitimate knowledge in the quantitative-qualitative data continuum. We then provide a brief overview of each of the articles comprising this special issue and make connections to the ways they compel us to think in the ‘beyond’. By interrogating these considerations (and those exceeding this article), we can work towards a transformation of public health research, policy and practice, and the knowledge systems they are embedded within. The aim of this article is to underscore the urgent need to confront racism in public health and to reimagine and remake the field towards advancing health equity for racialized communities and for all.
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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 | 0.065 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.031 | 0.118 |
| Scholarly communication | 0.032 | 0.031 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.016 | 0.038 |
| Insufficient payload (model declined to judge) | 0.002 | 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".