Around the X: Reflections on Straight Edge, Visuality, and Identity Boundaries
Bibliographic record
Abstract
Abstract | When Minor Threat sang “I’ve got straight edge” back in the 1980s they could not imagine the proportions the straight edge message would achieve in a few years. In São Paulo, the straight edge subculture was consolidated in the 1990s around a collective and its festival Verdurada, which is still active. The dynamics to establish the boundaries of the straight edge identity are complex, usually dealing with several visual aspects. This work aims to shed light onto the visuality of the straight edge subculture and the Verdurada, discussing the place(s) that straight edgers occupy in the 21st century.Résumé | Lorsque le groupe Minor Threat chanté « I’ve got straight edge » dans les années 1980, il ne pouvait pas imaginer les proportions que le message straight edge pourrait atteindre. À São Paulo, le straight edge sous-culture a été consolidée dans les années 1990 autour du festival Verdurada et son collectif homonyme, qui est toujours actif. La dynamique d’établir les limites de l’identité straight edge est complexe, généralement constitué par plusieurs aspects visuels. Ce travail vise à analyser la visualité de la sous-culture straight edge et la Verdurada, en discutant la place qui les straight edgers occupent dans le 21e siècle.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".