Bibliographic record
Abstract
In this collection of poems, I narratively engage each chapter in Octavia E. Butler’s Kindred to examine disability and Black life anew. My engagement with the tale of longing, time-travel, and slavery that Butler weaves together in the novel is moved by an interpretive, or a phenomenological, disability studies approach, where “the experience of disability, our own or that of others, becomes the scene where we can frame how we experience embodied existence and, thus, disability becomes a place where culture can be examined anew, again and again” (Titchkosky & Michalko, 2012/2017, p. 77). Interpretive disability studies surfaces necessary questions around how we make sense of boundaries that distance ‘normal’ from ‘non-normal.’ Haitian author, Edwidge Danticat’s (2018) desire to make sense of separation is what brought her, in part, “…to the internal geography of words and how they can bridge sentences” (para. 10). Following in Danticat’s footsteps, while remaining indebted to the wisdom of Black women storytellers' writ large, I hope to understand the separations among the characters of Kindred, namely among Kevin, Rufus, Alice, Alice’s mother, Hagar, Dana and her ancestors, and Tom Weylin, and, in so doing, emerge, through poetry, from a geography of words charted by Butler and again encountered. The below poetic emergence reveals all boundaries as bridged, showing how disability can become a place where culture can be examined anew. For Dana and her ancestors, perhaps we might wonder about what it means to be in kindred with notions of normal and non-normal, and to live in kindred with one another.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".