Bibliographic record
Abstract
In the first issue of the Journal of Literary Multilingualism, we collected together a range of scholars assessing and debating the field of Literary Multilingualism Studies, but we also realised that a single issue could only scratch the surface of this dynamic and growing field of study. Moreover, we were aware of some absences and blind spots and of the need to be constantly revising, questioning and examining the field. This forum, appearing in each issue of the journal, aims to continue the conversation started in Issue 1: it is a space for shorter, more informal reflections on the field and its future, in forms that might include position papers, dialogues between scholars, roundtable discussions, responses to articles within the journal, and responses to recent multilingualism conferences or events. We welcome proposals for Forum contributions, particularly from marginalised perspectives or on neglected aspects of literary multilingualism. Please contact us directly to discuss ideas. For this first forum, we asked David Gramling, who has recently spoken about ‘breaking up’ with multilingualism, how his attitude to the field has changed in recent years, and why. We also asked him to think of the direction Literary Multilingualism Studies should take in the future, in terms of its objects, its theories, and the genres it treats.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".