Networked Scars: Tattooed Bodies after Breast Cancer
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
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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