<i>Tag, you're different:</i> the interrupted spaces of children at risk of anaphylaxis
Bibliographic record
Abstract
Several contributions to understanding the emotional aspects of everyday lives have been made by geographers. What we undertake in this research is to focus that lens on the emotional context of experiences of children at risk and in particular on anaphylactic risk-scapes, particularly within the school environment. This research attempts to go beyond the policy response by privileging the voices of the affected children (and their parents) in order to understand the emotionality of their risk experience; how it is articulated and negotiated in place, and how bodily boundaries are interrupted. Qualitative methods were used to explore children's perceptions of, and experiences with, anaphylactic allergy in the school environment. In-depth interviews were conducted with 10 children (aged 8–12 years) and 10 adolescents (aged 13–17 years) and their parents. Children were also asked to draw a picture of ‘what it was like to live with a severe food allergy’ and to then explain their illustration. Results revealed how the spaces of children at risk of anaphylaxis were interrupted; social spaces were interrupted through their bodily experiences of risk as they negotiated in and through school environments. These findings suggest allergic children contest school and policy constructions of ‘safe place’ through the interrupted spaces and bodily disruptions of emotion.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".