More than Global? A Roundtable Discussion
Bibliographic record
Abstract
Abstract In 2016, Duke University press published a book-length dialogue between two leading literary and cultural critics, Ranjan Ghosh and J. Hillis Miller, Thinking Literature Across Continents , which provides the starting point for this discussion forum. Particularly pertinent both for this forum and, more generally, special issue, in the fifth chapter of Thinking Literature Across Continents , Ghosh examines the possibilities of “more than global” as a lens through which to assess the possibilities not only of literary but of all thought processes. Here, five scholars – Rob Wilson, Sandeep Banerjee, Frank Schulze-Engler, Zahi Zalloua, and Ming Xie – meditate on the potential of Ghosh‘s “more than global” thesis. Finally, Ghosh responds to these meditations in the light of his ever-developing thesis.
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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.028 | 0.040 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".