Bibliographic record
Abstract
Publishing professionals concern themselves with the textual and graphic details of the content they publish. Editors, specifically, are responsible for checking and correcting the details of language, style, format, and typography. As precise as editors are paid to be, the nomenclature employed by editing professionals to identify the type of editing required by manuscripts, to communicate about editorial activity, and to evaluate editorial work is, ironically, imprecise. This article identifies the nomenclature used in the publishing industry, the scholarly publishing sector specifically, to label, describe, and discuss editorial activity. It provides an overview of terminology and definitions garnered from a review of literature on the subject of editing. It also presents the results of an original survey of editorial professionals, conducted online, which solicited responses to questions about terms and valuations in the practice of copy-editing. Based on these data, the article draws conclusions and makes suggestions about potential courses of action for standardizing editorial nomenclature in the publishing industry.
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 | 0.036 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| 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".