Embodying Liminality: The Disruptive Potentialities of Medically Unexplained and In/Visible Chronic Illness
Bibliographic record
Abstract
This thesis explores how fluctuating, ambiguous, and medically unexplained illnesses might be quintessentially postmodern conditions that disrupt taken-for-granted medical and socio-cultural classifications. Drawing on qualitative interviews with people with the “contested” diagnosis of Fibromyalgia Syndrome (FMS), I offer the concept of embodying liminality to describe the disruptive possibilities of embodiment that resists containment and instead resides in the liminal space in-between health/illness, dis/ability, in/visibility, and absence/presence. Situating this analysis within a broader context of neoliberalism and disablism/ableism, I argue that the liminal embodied experiences of people with unexplained, contested, and in/visible illnesses might provide a critique of the increasing pressure we are all under to embody and enact narrow cultural ideals of healthiness, fitness, and competence. Ultimately, this thesis hopes to contribute to the deconstruction of damaging dichotomous categories and the harmful illusion of the invulnerable and perfect(able) body, and to reveal the liberating potentialities of embracing the fluid spectrum of embodiment.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".