Tattooing and Civilizing Processes: Body Modification as Self‐control*
Bibliographic record
Abstract
Malgré la révolution en cours dans le tatouage en Amérique du Nord, les universitaires restent attachés à l'idée que les « fanatiques du tatouage » seraient des inadaptés sociaux. Dans cet article, des données obtenues au cours d'une observation participante de trois ans parmi des fanatiques du tatouage au Canada ouvrent la porte à une critique des interprétations psycho‐sociales privilégiées du tatouage comme étant une conduite irrationnelle, impliquant des risques (voir Carroll et coll., 2002; Roberts et Ryan, 2002). À la lumière de la sociologie figurationnelle (Elias, 1983; 1994; 1996), le tatouage est ici vu comme un acte de communication à caractère sociable et régi affectivement plutôt que comme un cas pathologique d'automutilation. Despite the ongoing revolution in the use of tattoos in North America, academic understandings of tattooing remain grounded in conceptions of “tattoo enthusiasts” as social misfits. In this paper, data from three years of participant observation with tattoo enthusiasts in Canada help critique preferred social‐psychological interpretations of tattooing as irrational, “risk‐taking behaviour” (see Carroll et al., 2002; Roberts and Ryan, 2002). Through the lens of figurational sociology (Elias, 1983; 1994; 1996), tattooing is interpreted in this paper as a pro‐social and affectively regulated act of communication, rather than a pathological instance of self‐injury.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".