Bibliographic record
Abstract
This study examines an archive of peer-reviewed articles in which physical, financial, and psychological harm were used metaphorically as source material to elaborate on more literal concerns about arts-based research. We examined the ways metaphoric language shapes our notions of arts-based research by asking: What social conditions are enabled by the prevalence of danger discourse in relation to arts-based research? We explored common metaphorical themes including exploration, landscape, and warfare. Three categories of metaphor are discussed: danger as a cautioning agent or direct danger; reversals of danger or metaphors that frame danger as desirable; and danger that involves the loss of legitimacy. Metaphorical danger discourse serves a number of functions: reproducing cultural norms, promoting caution, encouraging risk taking, revealing networks of institutional power, and polarizing debate. This article ends with a discussion of the implications of the use of metaphorical danger discourse in the field of art education.
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.
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 | 0.029 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.015 | 0.064 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.005 |
| 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".