Bibliographic record
Abstract
This paper investigates the growing trend of mastectomy tattoos as an alternative to reconstruction and their implication on the (de)regulation of women's bodies in the digital context. I explore how tattoos are incorporated into a "breast cancer culture" (King, 2010) as a form of self-care in the recreation of areola pigmentation after breast reconstructive surgery and in cosmetic masking of post-operative mastectomy scars. I am concerned with how online discourses of tattooing practices are drawing women's bodies into an emergent 'biopolitics' (Foucault, 1990; Rose, 2001), a productive type of power concerned with the risk management of a 'biomedicalized subject' where women are encouraged to care for their health through informed decisions via online media (Pitts, 2004) and through consumption and beautification techniques in line with normative femininity (King, 2006). Yet, online media can potentially operate as a site for the creation of new publics wherein women can retell the stories of their bodies through new practices of inscription outside of medicalized and masculinist reconstruction narratives. I perform a discourse analysis of Canadian expert and popular discourses in health websites, plastic surgery and cosmetic service websites, tattoo parlour websites and in social media, including P.ink, (an organization that supports mastectomy tattoos). I argue that within digital media competing medical, pop cultural and feminist narratives intersect in ways that can contribute to an "awkward feminist politics" (Smith-Prei & Stehle, 2016) where women's hybridized medical, digital, tattooed bodies can operate as material obstacles to normative correlations between health, femininity and sexuality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".