The use of comments as a strategy in the accountable editing of academic texts
Bibliographic record
Abstract
Existing guidelines regarding the editing of academic texts (compare those of the SU Language Service; internationally, also those of the Institute of Professional Editors [IPEd]; the Editors’ Association of Canada [EAC]; and the Council of Australian Societies of Editors [CASE]) emphasise that editors should not alter the content and structure of this type of text. However, in practice, it is not always clear how editors should deal with problems in the content and structure of such texts. The goal of this study is to provide guidelines for editors of academic texts who adhere to a process approach. The editing of eight academic articles is investigated, with specific reference to the use of comments as a strategy to empower the author to effect changes regarding content and structure. Comments by the editors of these articles are described, interpreted and evaluated in order to formulate guidelines for an accountable editing strategy.
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 | 0.167 | 0.335 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| 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".