Bibliographic record
Abstract
I have been ruminating lately on the notion of “white space,” “writer’s block,” and selfhood following a traumatic brain injury that caused word-finding problems during the acute phase of my recovery. I have coined the term, “neuropoetics” to denote the attempt to describe in poetry and flash fiction the experience of “drawing a blank” or being silenced by a medical condition. Attached you will find three poems for your consideration: “Memory Villanelle,” “The Invisible Woman,” and “In the Rehab Waiting Room.” I was inspired by Laurie Clements Lambeth’s statement regarding the use of formal structure in her poetry about her Multiple Sclerosis, the blurring of bodily boundaries: “I needed the cage of a villanelle—so restrictive, in that very few lines can truly further the poem along, and yet so obsessive a form—to house the poem” (171). I have tried the same technique to describe the connection between memory and identity. “The Invisible Woman” takes as much from the Marvel universe as H.G. Wells; in it, I use the figure of Sue Storm and her dubious gift of being ignored as representative of the experience of an “invisible disability,” or something neurological rather than physically identifiable. Finally, “In the Rehab Waiting Room” was accidentally inspired by Elizabeth Bishop’s famous poem, as per the epigraph. It was an early attempt to capture the feeling of being a “blank slate” or having that post-traumatic “blank stare” that avoids eye contact with others
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".