Bibliographic record
Abstract
Misha [Nogha/Chocholak] is a Métis/Cree author, editor, poet, and musician whose eclectic career includes longer fiction, chapbooks, prose poems, libretti, and nonfiction. As this chapter shows, Red Spider White Web (1990) marks Misha as an important figure for both Indigenous futurisms and Indigenous cyberpunk because the novel focuses on ecological and feminist themes and eschews the masculinist fantasies that were ascribed to print cyberpunk’s earliest wave. Her work in Red Spider White Web connects those vulnerable bodies common to cyberpunk to Indigenous histories of colonialism, medical abuse, and sexual violence, which suggest that by writing of survival in this bleak world, Misha both deconstructs the raced and gendered binaries that inform many contemporaneous cyberpunks and creates a space for ‘survivance.’ In so doing, Misha offers a unique perspective on cyberpunk’s fascination with the posthuman, always returning to a posthuman body that is vulnerable, disabled, queer, and non-white, going so far as to transform cyberpunk’s punk attitude into a trickster performance. Thus, in a cultural mode associated with digital frontiers, Misha continues to redirect cyberpunk’s posthuman politics toward the original frontiers—occupied native land—finding survivance in even the most bleakly dystopian futures.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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".