Embodiment, Autoethnography, Performance Poetry: Living with Severe Traumatic Brain Injury
Bibliographic record
Abstract
This presentation offers neurodivergence embodied, autoethnography, and performance poetry. The confluence of acquiring severe traumatic brain injury combined with exposure to concepts and paradigms while pursuing a graduate degree in Disability Studies, catalyzed emergence and triggered development of my disabled identity. The brain damage acquired causes issues of decoding/deciphering/processing, which in turn triggers and/or produces episodes of temporal dissonance. When these shifts in timing occur, they have tremendous impact on rational thought processes and emotional stability. The salient aspects of my new life – emotional sensitivity and volatility – may on the surface seem detrimental and undesirable; however, I celebrate these qualities as they greatly enhance my identification with and empathy for others, which in turn drive my artistic, social, cultural, political expression, quest for community and belonging. While temporal dissonance is unlikely to occur during this planned short presentation, I will relate and provide the audience with windows on largely hidden and little understood forms of impairment. Note: To hear recitation of some of these poems, check out fellow VIBE presenter Cheryl Green’s podcast: http://whoamitostopit.com/pigeonhole-podcast-17-autoethnographic-poetry/ The original presentation at VIBE was accompanied by music from Miles Davis and Marcus Miller’s 1987 album Music from Siesta. Readers are encouraged to listen to this album via their music platform of choice while reading the following poetry. Youtube link to the album: https://www.youtube.com/watch?v=ZuvtNL_jyeQ
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".