Pain and the field of affordances: an enactive approach to acute and chronic pain
Bibliographic record
Abstract
Abstract In recent years, the societal and personal impacts of pain, and the fact that we still lack an effective method of treatment, has motivated researchers from diverse disciplines to try to think in new ways about pain and its management. In this paper, we aim to develop an enactive approach to pain and the transition to chronicity. Two aspects are central to this project. First, the paper conceptualizes differences between acute and chronic pain, as well as the dynamic process of pain chronification, in terms of changes in the field of affordances. This is, in terms of the possibilities for action perceived by subjects in pain. As such, we aim to do justice to the lived experience of patients as well as the dynamic role of behavioral learning, neural reorganization, and socio-cultural practices in the generation and maintenance of pain. Second, we aim to show in which manners such an enactive approach may contribute to a comprehensive understanding of pain that avoids conceptual and methodological issues of reductionist and fragmented approaches. It proves particularly beneficial as a heuristic in pain therapy addressing the heterogenous yet dynamically intertwined aspects that may contribute to pain and its chronification.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.046 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".