The intersectional implications of a quantitative epistemology in pain care and research
Bibliographic record
Abstract
Background: There is a growing interest in understanding the long-standing tension between subjective experience and objective measurement, with a focus on better understanding personal or lived experience. However, quantitative pain measurement is itself a complicated practice that is rarely examined. The method does not exist in a vacuum but along a historical trajectory that we believe to be worth unpacking. Aims: We seek to highlight (1) the problematics associated with a systemic reliance on quantitative tools that are themselves validated via statistical methods; (2) what alternatives already exist, regardless of their logistical shortcomings; and (3) the actual and possible consequences of continuing a trajectory of data-based pain rating. Methods: We present historical and contemporary case studies through theoretical frames that help the reader understand the social construction of pain as a phenomenon whose quantification has been justified with statistical approaches. Results: Relying on quantitative data for a pain rating that is perceived as more valid, reliable, and efficient-a triad that has come to represent the ideal pain measurement instrument-risks entrenching both patient/participant and clinician/researcher in systems of computation and control. This is detrimental to society's most vulnerable populations. Conclusions: Patients, practitioners, and social scientists all have an opportunity to reframe their understanding of pain measurement as medical practice to build more equitable spaces in pain medicine.
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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 | 0.163 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.014 | 0.206 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".