The Garden in the Machine: Grace Lee Boggs’s Living for Change: An Autobiography and Detroit’s Urban-Agrarian Future
Bibliographic record
Abstract
'The Garden in the Machine: Grace Lee Boggs’s Living for Change: An Autobiographyand Detroit’s Urban-Agrarian Future" is chapter two of part one: Neoimperialisms, Neoliberalisms, Necropolitics in Asian American Literature in Transition, 1996-2020: Volume 4, edited by Betsy Huang, Victor Román Mendoza This volume examines the concerns of Asian American literature from 1996 to the present. This period was not only marked by civil unrest, terror and militarization, economic depression, and environmental abuse, but also unprecedented growth and visibility of Asian American literature. This volume is divided into four sections that plots the trajectories of, and tensions between, social challenges and literary advances. Part One tracks how Asian American literary productions of this period reckon with the effects of structures and networks of violence. Part Two tracks modes of intimacy - desires, loves, close friendships, romances, sexual relations, erotic contacts - that emerge in the face of neoimperialism, neoliberalism, and necropolitics. Part Three traces the proliferation of genres in Asian American writing of the past quarter century in new and in well-worn terrains. Part Four surveys literary projects that speculate on future states of Asian America in domestic and global contexts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".