Bibliographic record
Abstract
While a growing body of research is uncovering the aetiology and effective treatments for allergy, research that assess the broader ethical implications of this disease is lacking significantly. This article will demonstrate both the paucity of academic research concerning ethical implications in allergy and explain why ethical analysis is integral to formulating effective health strategies for allergic disease. An exhaustive literature search of publications in French and English identified less than 35 academic articles focussed on the topic of ethics and allergy; this is a miniscule number when compared to the amount of articles published on ethical issues related to other chronic illnesses, such as obesity. It is important to demonstrate to allergy specialists the need for, and utility of, further incorporating ethical analyses in allergology; the current success of Ethical, Legal, Social Implications (ELSI) research programmes in human genetics and nanotechnology will serve as notable examples. Indeed, future research and innovation in allergy will undoubtedly encounter ethical dilemmas and the allergology community should play a significant role in helping to address these issues. However, incorporating ethical analyses in allergology does not imply that the allergology community must acquire extensive knowledge in bioethics; instead, interdisciplinary research that incorporates expertise from allergology and bioethics would enable allergy specialists to advance critical knowledge development in this largely overlooked domain of study.
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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 | 0.184 | 0.256 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".