Thinking ‘beyond’: critical reflections on race, racism, and the field of public health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it