Slow Writing; or, Getting Off the Book Standard: What Can Journal Editors Do?
Bibliographic record
Abstract
Lindsay Waters has argued elsewhere that the dominant position of the book in humanities scholarship is ripe for challenge. If this is so – if there are signs that the essay form is poised to take the ascendant – what might be the journal editor's role in this venture? In this essay, adapted from the keynote address presented to the Council of Editors of Learned Journals at the 2007 MLA Convention, Waters argues persuasively for a move to “the well-wrought, slowly gestated essay” as the gold standard of scholarship and for a return to fundamentals – to careful thought, to the well-constructed sentence, to judgement – in the humanities, where, he contends, the productivity problem is not one of quantity but one of quality.
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 | 0.070 | 0.390 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.046 | 0.054 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.010 |
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".