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 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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".