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 12, Number 1Aissa HANIFI, University of Chlef, AlgeriaAmelia Maria Cava, Università fdi Napoli Federicio II, Naples, ItalyAna Maria Costa Lopes, Higher School of Education of the Polytechnic Institute of Viseu, PortugalAndrés Canga, University of La Rioja, SpainAntonio Piga, University of Cagliari, ItalyAyman Khafaga, Suez Canal University, EgyptBahram Kazemian, Islamic Azad University, IranChunlin Yao, Tianjin Chengjian University, ChinaDaniel Ginting, Universitas Ma Chung, IndonesiaDon Anton Balida, International College of Engineering and Management, OmanElena Alcalde Peñalver, University of Alcalá, SpainEmine Bala, Tishk International University, IraqGhadah Al Murshidi, The United Arab Emirates University, UAEHameed Yahya Ahmed Al-Zubeiry, Al-Baha University, Saudi ArabiaHANY ALI MAHMOUD ABDELFATTAH, Minia University, EgyptHossein Salarian, University of Tehran, IranHouaria Chaal, Hassiba Ben Bouali University of Chlef, AlgeriaJânderson Coswosk, Instituto Federal do Espírito Santo, BrazilJasna Potocnik Topler, University of Maribor, SloveniaKanthimathi Krishnasamy, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, IndiaKenan Yerli, Sakarya University, TurkeyLeila Lomashvili, Shawnee State University, USALi Ping Chang, Department of Applied Foreign Languages, National Taipei College of Business, TaiwanMaria del Mar Sanchez Ramos, University of Alcalá, SpainMaria Isabel Maldonado Garcia, Al-Andalus Institute of Languages University of Lahore, PakistanMohamad Fadhili bin Yahaya, Universiti Teknologi Mara Perlis Branch, MalaysiaMohammad Hamad Al-khresheh, Northern Border University, Saudi ArabiaMorteza Amirsheibani, Ferdowsi University of Mashhad, IranMuhammed Ibrahim Hamood, University of Mosul, IraqMustafa Ar, Ar-Raniry State Islamic University, IndonesiaNitin Malhotra, St. Theresa International College, Bangkok, ThailandÖzkanal, Ümit, Eskisehir Osmangazi University Foreign Languages Department, TurkeyPatnarin Supakorn, Walailak University, ThailandRashad Al Areqi, Al Baha University, KSARoberto Martínez Mateo, UNIVERSITY OF CASTILE LA-MANCHA, SpainRommel Maglaya, Cambridge IGCSE Examiner, PhilippinesSantri Djahimo, Nusa Cendana University, IndonesiaScott-Monkhouse Anila Ruth, Language Centre – University of Parma (Italy), ItalyŞenel, Müfit, 19 Mayıs University, TurkeyShalini Yadav, Compucom Institute of Technology and Management, IndiaShangrela Genon-Sieras, Mindanao State University, Main Campus, PhilippinesSukhdev Singh, National Institute of Technology Patna, IndiaTeguh Budiharso, State Institute of Islamic Studies (IAIN) of Surakarta, Indonesia, IndonesiaWenjie Shi, Central University of Finance and Economics, China
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 | 0.057 | 0.560 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.185 | 0.120 |
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".