Investigating the problem of bullying through photo elicitation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Bullying is a tenacious problem in schools. Usual strategies that attempt to regulate behaviour and improve interpersonal relationships have not yielded significant and sustained change in school cultures of violence. Usually overlooked in programmes, policies and research are indications of how social differences are a factor of bullying behaviours. Such differences mirror broader categories that are socially significant, such as race, religion, gender, physical and mental ability and sexual orientation. We employed photo elicitation methods to acquire and assess students' responses to images we collected of children and youth who represent a wide spectrum of human diversity. We asked participants to 'think out loud' about who would mostly likely be targeted for bullying and to explain why. Our analysis of the data indicates that our participants are aware of how social difference is linked to bullying. The themes we identify lead us to endorse bridging the gap between current anti-bullying strategies and theory and approaches that account for social difference. Keywords: bullyingsocial differenceidentity politicseducational policy Acknowledgements The authors would like to thank Dr Stephen Minton for his helpful feedback. Notes 1. Names of all participants in this study are pseudonyms. 2. We acknowledge here that, as a political strategy, descriptors such as 'nigger', 'queer' and 'bitch' have been reclaimed by some who are targeted in these ways as positive identities despite how such words have been, and continue to be, used in the service of oppression and violence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it