Reviewer Acknowledgements for World Journal of English Language, Vol. 13, No. 2
Bibliographic record
Abstract
World Journal of English Language wishes to acknowledge the following individuals for their assistance with peer review of manuscripts for this issue. Their help and contributions in maintaining the quality of the journal are greatly appreciated.World Journal of English Language is recruiting reviewers for the journal. If you are interested in becoming a reviewer, we welcome you to join us. Please contact us for the application form at: wjel@sciedupress.comReviewers for Volume 13, Number 2Abdulfattah Omar, Prince Sattam Bin Abdulaziz University, Saudi ArabiaAcep Unang Rahayu, Poltekpar NHI Bandung, IndonesiaAli Hussein Hazem, University of Patras, GreeceAmelia Maria Cava, Università di Napoli Suor Orsola Benincasa, ItalyAnna Maria Kuzio, University of Zielona Gora, PolandAntonio Piga, University of Cagliari, ItalyBahram Kazemian, Islamic Azad University, IranChunlin Yao, Tianjin Chengjian University, ChinaDaniel Ginting, Universitas Ma Chung, IndonesiaDeena Elshazly, Arab Academy for Science, Technology and Maritime Transport, EgyptDon Anton Balida, International College of Engineering and Management, OmanElena Alcalde Peñalver, University of Alcalá, SpainFatma Abusrewel, The University of Tripoli, LibyaFrans Sayogie, Universitas Islam Negeri Syarif Hidayatullah Jakarta, IndonesiaG. Bhuvaneswari, Vellore Institute of Technology, Chennai, India, IndiaHameed Yahya Ahmed Al-Zubeiry, Al-Baha University, Saudi ArabiaHANY ALI MAHMOUD ABDELFATTAH, Minia University, EgyptHerman, Universitas HKBP Nommensen Pematangsiantar, IndonesiaHossein Salarian, University of Tehran, IranHouaria Chaal, Hassiba Ben Bouali University of Chlef, AlgeriaInayatullah Kakepoto, Quaid-e-Awam University of Engineering Science & Technology, Nawabshah, PakistanIryna Lenchuk, Dhofar University, OmanJânderson Coswosk, Instituto Federal do Espírito Santo, BrazilKanthimathi Krishnasamy, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, IndiaKaya özçelik, Atılım University, TurkeyKhaled Elkotb Elshahawy, University of Tabuk, Tayma Campus, Saudi ArabiaL. Santhosh Kumar, Kristu Jayanti College (Autonomous), IndiaLeila Lomashvili, Shawnee State University, USALi Ping Chang, Department of Applied Foreign Languages, National Taipei College of Business, TaiwanMaria Isabel Maldonado Garcia, University of the Punjab, PakistanMohamad Fadhili bin Yahaya, Universiti Teknologi Mara Perlis Branch, MalaysiaMorteza Amirsheibani, Ferdowsi University of Mashhad, IranMuhammad Mooneeb Ali, HED punjab, PakistanMuhammed Ibrahim Hamood, University of Mosul, IraqNing Li, Guangdong Pharmaceutical University (GDPU), ChinaNitin Malhotra, Gobindgarh Public College, IndiaNuriadi Nuriadi, University of Mataram, IndonesiaOlena Andrushenko, Universität Augsburg, GermanyÖzkanal, Ümit, Eskisehir Osmangazi University Foreign Languages Department, TurkeyRashad Al Areqi, Al Baha University, KSARoberto Martínez Mateo, UNIVERSITY OF CASTILE LA-MANCHA, SpainSafi Eldeen Alzi’abi, Jerash University, JordanSaif Ali Abbas Jumaah, University of Mosul College of Arts Dept. Media and English Communication. IraqSantri Djahimo, Nusa Cendana University, IndonesiaŞenel, Müfit, 19 Mayıs University, TurkeyServais Dieu-Donné Yédia DADJO, University of Abomey-Calavi, BeninShalini Yadav, Compucom Institute of Technology and Management, IndiaValeria Silva de Oliveira, Marinha do Brasil, Brazil
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 | 0.044 | 0.446 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.113 | 0.075 |
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".