Critically engaging vulnerability: Rethinking oral health with vulnerabilized populations
Bibliographic record
Abstract
This paper is the third in a series of narrative reviews challenging core concepts in oral health research and practice. Our series started with a framework for Inclusion Oral Health. Our second review explored one component of this framework, looking at how intersectionality adds important complexity to oral public health. This current manuscript drills into a second component of Inclusion Oral Health, exploring how labels can lead to 'othering' thereby misrepresenting populations and (re)producing harms. Specifically, we address a common oral public health label: vulnerable populations. This term is commonly used descriptively: an adjective (vulnerable) is used to modify a noun (population). What this descriptor conceals is the 'how,' 'why,' and 'therefore' that leads to and from vulnerability: How and why is a population made vulnerable; to what are they vulnerable; what makes them 'at risk,' and to what are they 'at risk'? In concealing these questions, we argue our conventional approach unwittingly does harm. Vulnerability is a term that implies a population has inherent characteristics that make them vulnerable; further, it casts populations as discrete, homogenous entities, thereby misrepresenting the complexities that people live. In so doing, this label can eclipse the strengths, agency and power of individuals and populations to care for themselves and each other. Regarding oral public health, the convention of vulnerability averts our research gaze away from social processes that produce vulnerability to instead focus on the downstream product, the vulnerable population. This paper theorizes vulnerability for oral public health, critically engaging its production and reproduction. Drawing from critical public health literature and disability studies, we advance a critique of vulnerability to make explicit hidden assumptions and their harmful outcomes. We propose solutions for research and practice, including co-engagement and co-production with peoples who have been vulnerabilized. In so doing, this paper moves forward the potential for oral public health to advance research and practice that engages complexity in our work with vulnerabilized populations.
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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 | 0.081 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.067 |
| Scholarly communication | 0.023 | 0.043 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".