Book Review | Intoxicated: Race, Disability, and Chemical Intimacy Across Empire, by Mel Y. Chen (Duke University Press, 2023)
Bibliographic record
Abstract
Intoxicated: Race, Disability, and Chemical Intimacy Across Empire (2023) is as richly interdisciplinary as their previous book, Animacies : Biopolitics, Racial Mattering, and Queer Affect (2012).Both texts traverse a wide range of methods and sites of inquiry, seeking political affinities between seemingly disparate forms of oppression by tracing the circulation of materials.In Intoxicated, Chen extends their exploration to "the fibrillations of what we might call an affective nexus between race and disability" (1), where "chemical intimacy" is a framework for thinking intoxication, race, and disability together (2).Chen transgresses disciplinary boundaries, bringing a range of critical scholars into conversation, including Jasbir Puar's (2017) articulation of debility, Nirmala Erevelles and Andrea Minear's (2010) work on disability and race, Jack Halberstam's (2020) concept of queer failure, along with many others.Weaving between theoretical lineages including queer, Black, disability, East Asian, decolonial, and posthumanist studies, Intoxicated makes important contributions to these fields while also carving its own space between and beyond them.The depth and breadth of Chen's multi-sited analysis is rooted in the neurodiversity of the author's own mind where "there was so much going on" (15).The book moves through mixed scenes of affect and intoxication, including the archives of John Langdon Down, the politics of opium in China and in London's Chinatown, the Opium Wars, letters by Lin Tse Hsu to Queen Victoria, the Aboriginals Protection and Restriction of the Sale of Opium Act in Australia, artist Fiona Foley's artworks
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.019 |
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".