Bibliographic record
Abstract
This poem is informed by the relationships between gender, race, chronic pain, hysteria, and the role of dominant discourses in shaping interpretations of bodily and psychic pain. Drawing on my lived experience as a non-binary person with chronic vulvar pain, or vulvodynia, I challenge the psychiatrization of chronic pain and propose hysteria as a potential state of resistance and refusal (Dumaresque, 2019). I weave fog throughout this poem as a metaphor that captures pain, madness, and perception. Fog symbolizes disruption and disorientation; yet, fog also gestures to the potentiality of being displaced from normative insight (Bruce, 2017). I engage William Connolly’s (2010) reading of perception as formed through discipline to think through the silent but subversive waves of knowledge and power that carve the lenses through which we story ourselves and others (Erickson, 2016). As Thomas King (2003) writes, “the truth about stories is that’s all we are” (p. 32). This poem is situated in a reading of madness and hysteria as sites of affective protest (Dumaresque, 2019). I ask, what can be resourced from our becoming un-hinged? This poem contributes to mad knowledge that is intersectional and in-service to disrupting medical and psychiatric violence, whiteness, hetero/cis-governance, and “compulsory able-bodymindedness” (Sheppard, 2018, p. 59).
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.157 | 0.055 |
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".