Constraint Poetry on Disability and Disability as Constraint
Bibliographic record
Abstract
This project elevates constraint poetry – a genre with restrictions like metre, as opposed to free verse – as a vessel for sharing disabled experiences. This area of research is especially important for advancing representations of the disabled, a stereotyped and underrepresented demographic. Sharing a constraint poem that I wrote about my multiple sclerosis (MS), the relevance of constraint poetry for disability studies manifests in the writing constraint mirroring the occasionally invisible, yet restricting, nature of MS. This poem catalyzed my research into applying constraint poetry to disability studies, forming the basis of the dissertation that I plan to write in my upcoming master’s in creative writing. Current scholarship in the field includes applying constrained writing to prisoners and the enslaved but lacks inclusion of those with disabilities. My future plans include drawing upon perspectives of disabled people and medical professionals to depict the spectrum of constraint produced by disability, as well as creating new constraints that can better express specific symptoms. This dissertation would be a collection of my constraint poetry on different disabilities and would be the first of its kind.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".