Crisis of Erasure
Bibliographic record
Abstract
In this paper, we use the topic of breast cancer as an example of health crisis erasure in both informational and institutional contexts, particularly within the transgender and gender-nonconforming population. Breast cancer health information conforms and defaults to conventional cultural associations with femininity, as is the case with pregnancy and other “single-sex” conditions (Surkan, 2015). Many health information and research practices normalize sexualities, pathologize non-normative gender (Drescher et al., 2012; Fish, 2008; Müller, 2018), and fail to recognize gender-nonconforming categories (Frohard‐Dourlent et al., 2017). Because breast cancer health information is sexually normalized, an information boundary exists for the LGBTQ+ community, particularly among transgender and gender-nonconforming adults who are at greater risk of discrimination in healthcare settings (Casey et al., 2019). Transgender and gender-nonconforming people experience unique marginalization and risk with respect to breast cancer. We call upon and propose library and information research, education, and practice opportunities inclusive of the health information needs of transgender and gender-nonconforming populations.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".