Passive tolerance and productive uncertainties in food allergy immunotherapy biomedical practices
Bibliographic record
Abstract
There has been little sociological research on food allergy in North America, despite the fact that it impacts a significant population in the US and Canada. Immunotherapy for food allergy is now a prominent area of investigation and experts argue it is one dimension of a 'paradigm shift' that encourages exposure to food allergens in contrast to avoidance of them. This article reports on an ethnographic investigation of immunotherapy for food allergy research and practice between the years 2015 and 2020. I report on analyses of N = 31 in-depth interviews with spokespersons with different types of engagement in immunotherapy and over 100 h of ethnographic observations at clinical and scientific conferences. My research revealed the entangled ontologies of food and drug in immunotherapy and related uncertainties in the diagnoses of food allergy, which catalysed further uncertainty about who is receiving and who should receive immunotherapy (IT). Relatedly, there are uncertainties about the goals and/or outcomes of IT. I elaborate on previous sociological work about how uncertainty is valued in medicine and science. I argue shared uncertainties and mutual scepticism in IT co-exist, representing a passive (productive) tolerance and reticently accepted form of tension in the domain, which have resulted in calls for intra-professional and inter-professional collaborations and the involvement of other stakeholders in knowledge making regarding food allergy immunotherapy.
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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 | 0.041 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.074 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".