Reviewer Acknowledgements for English Linguistics Research, Vol. 12, No. 1
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 12, Number 1Alina Andreea Dragoescu, University of Life Sciences "King Mihai I" from Romania, RomaniaGirolamo Tessuto, University of Naples, ItalyKazeem K. Olaniyan, Ladoke Akintola University of Technology, NigeriaNaom Nyarigoti, United States International University-Africa, KenyaNeda Chepinchikj, University of New South Wales (UNSW), AustraliaNoureddine Derki, Mustapha Stambouli Mascara University, AlgeriaPeace Chinwendu Israel, University of Education, GhanaVahid Hassani, Farhangian University, IranYuemin Wang, University of Chinese Academy of Sciences, ChinaZeineb Ayachi Ben Abdallah, Higher School of Digital Economy, Tunisia Best Regards,Camille SuEditorial Assistant, English Linguistics ResearchSciedu Press*************************************Add: Leslie St. Suite , Beaver Creek, Ontario, LB A, 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.972 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.008 |
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; both teacher heads agree on what is shown here.
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".