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Record W4390032014 · doi:10.25071/2369-7326.40352

"The Invisible Woman" and "In the Rehab Waiting Room"

2023· article· en· W4390032014 on OpenAlexaffvenue
Deborah Denise Herman

Bibliographic record

VenuePivot A Journal of Interdisciplinary Studies and Thought · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicRace, History, and American Society
Canadian institutionsYork University
Fundersnot available
KeywordsArchitectural engineeringEngineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.364
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

Same venuePivot A Journal of Interdisciplinary Studies and ThoughtSame topicRace, History, and American SocietyFrench-language works237,207