Bad Reputations: Memory, Corporeality, and the Limitations of Hacking’s Looping Effects
Bibliographic record
Abstract
Decades after Foucault’s Birth of the Clinic and History of Madness, the role of medicine in producing and sustaining classifications continues to be topical, as scholars have continued to critique normalizing judgments embedded in the practices of medicine, which stabilize identity categories within health care settings. A significant contributor to this area of scholarship, Ian Hacking has articulated a productive and extremely influential account of how certain “kinds” of people emerge hand-in-hand with the categories that are meant to classify them, examining not only medical practices, but a wide range of governmental, scientific, and cultural institutions that contribute to kind-making. In this paper, I examine limitations to Hacking’s looping effects thesis, in an effort to further explore how kind-making may be embodied through intersections of subjectivity, social identity, and the practices of medicine. Employing a field study of HIV/AIDS care in Vancouver, Canada, I push at some of the boundaries of Hacking’s account, attempting to add complexity and nuance by bringing to bear considerations of memory, resistance, and embodiment on the process of looping.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.114 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.002 | 0.010 |
| 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".