Bibliographic record
Abstract
Neil Lazarus is Professor of English and Comparative Literary Studies at Warwick University, UK. His PhD in Sociology from Keele University focused on the novels of Ayi Kwei Armah and lent itself to an interdisciplinary approach to the study of literature, which turned into his first book Resistance in Postcolonial African Fiction, published in 1990. He worked at Yale, Wesleyan, Louisiana State and Brown University before returning to the UK in 1999. Since then he has published a number of field-defining works, specifically issuing from a cultural materialist position, including over 40 articles, Nationalism and Cultural Practice in the Postcolonial World (1999), Marxism, Modernity and Postcolonial Studies (2002, edited with Crystal Bartolovich), The Cambridge Guide to Postcolonial Studies (2004) and After Iraq: Reframing Postcolonial Studies (2006, special issue of New Formations edited with Priyamvada Gopal). His most recent book, The Postcolonial Unconscious (2011), is a striking and insightful analysis of postcolonial studies past, present and future that will undoubtedly shape the development of the discipline. In this interview he talks about the idea of the postcolonial unconscious, the role of the intellectual and the future direction of his own work. I began by inviting him to chart his intellectual development from Resistance in Postcolonial African Fiction to The Postcolonial Unconscious.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.030 | 0.032 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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".