Bibliographic record
Abstract
This interdisciplinary work brings the humanities and social sciences into dialogue by examining issues such as globalized capital, discourses of antiterrorism, and identity politics. Essayists from the fields of postcolonial studies and globalization theory address the ethical and pragmatic ramifications of opposing interpretations of these issues and, for the first time, seek common ground. Contributors: Pal Ahluwalia, U of California, San Diego; Arjun Appadurai, New School U; Geoffrey Bowker, Santa Clara U; Timothy Brennan, U of Minnesota; Ruth Buchanan, U of British Columbia; Verity Burgmann, U of Melbourne; Pheng Cheah, U of California, Berkeley; Inderpal Grewal, U of California, Irvine; Ramon Grosfoguel, U of California, Berkeley; Barbara Harlow, U of Texas, Austin; Anouar Majid, U of New England; John McMurtry, U of Guelph; Walter D. Mignolo, Duke U; Sundhya Pahuja, U of Melbourne; R. Radhakrishnan, U of California, Irvine; Ileana Rodriguez, Ohio State U; E. San Juan, Philippine Forum, New York; Saskia Sassen, U of Chicago; Ella Shohat, New York U; Leslie Sklair, London School of Economics; Robert Stam, New York U; Madina Tlostanova, Russian Peoples’ Friendship U; Harish Trivedi, U of Delhi.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".