Reviewer Acknowledgements for English Linguistics Research, Vol. 10, No. 4
Bibliographic record
Abstract
English Linguistics Research (ELR) would like to acknowledge the following reviewers for their assistance with peer review of manuscripts for this issue. Many authors, regardless of whether ELR publishes their work, appreciate the helpful feedback provided by the reviewers. Their comments and suggestions were of great help to the authors in improving the quality of their papers. Each of the reviewers listed below returned at least one review for this issue. Reviewers for Volume 10, Number 4 Alina Andreea Dragoescu Urlica, University of Life Sciences, RomaniaGhadah Al Murshidi, The United Arab Emirates University, UAENaom Nyarigoti, United States International University-Africa, KenyaWin Whelan, St. Bonaventure University, USAYuemin Wang, University of Chinese Academy of Sciences, China Best Regards,Camille SuEditorial Assistant, English Linguistics ResearchSciedu Press*************************************Add: 9140 Leslie St. Suite 110, Beaver Creek, Ontario, L4B 0A9, CanadaTel: 1-416-479-0028 ext. 210E-mail 1: elr@sciedupress.com E-mail 2: elr@sciedupress.org Website: http://elr.sciedupress.com
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 | 0.060 | 0.487 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.092 | 0.064 |
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".