Reviewer Acknowledgements for World Journal of English Language, Vol. 13, No. 6
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 6Aissa HANIFI, University of Chlef, AlgeriaAli Hussein Hazem, University of Patras, GreeceAndrés Canga, University of La Rioja, SpainAnna Maria Kuzio, University of Zielona Gora, PolandAntonio Piga, University of Cagliari, ItalyAtyaf Hasan Ibrahim, University of Diyala, IraqAyman Rashad Rashid Yasin, Princess Sumaya University for Technology, JordanChunlin Yao, Tianjin Chengjian University, ChinaDaniel Ginting, Universitas Ma Chung, IndonesiaDon 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, IndonesiaHerman, Universitas HKBP Nommensen Pematangsiantar, IndonesiaHossein Salarian, University of Tehran, IranHouaria Chaal, Hassiba Ben Bouali University of Chlef, AlgeriaHussain Hamid Ali Ghazzaly, Al-Azhar University, EgyptInayatullah Kakepoto, Quaid-e-Awam University of Engineering Science & Technology, Nawabshah, PakistanJânderson Coswosk, Instituto Federal do Espírito Santo, BrazilJaypee R. Lopres, Gallup McKinley County Schools, New Mexico Public Education Department, USAJergen Jel A. Cinco- Labaria, Western Philippines University, PhilippinesKanthimathi Krishnasamy, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, IndiaKristiawan Indriyanto, Universitas Prima Indonesia, IndonesiaL. Santhosh Kumar, Kristu Jayanti College (Autonomous), IndiaLeila Lomashvili, Shawnee State University, USALi Ping Chang, National Taipei College of Business, TaiwanMohammad Hamad Al-khresheh, Northern Border University, Saudi ArabiaMohammed AbdAlgane, Qassim University, Saudi ArabiaMuhammad Farkhan, Universitas Islam Negeri Syarif Hidayatullah Jakarta, IndonesiaMuhammad Mooneeb Ali, HED punjab, PakistanMuhammed Ibrahim Hamood, University of Mosul, IraqMundi Rahayu, Universitas Islam Negeri Maulana Malik Ibrahim Malang, IndonesiaMusa Saleh, Qimam Al-Ulum Institute for Languages, Saudi ArabiaNing Li, Guangdong Pharmaceutical University (GDPU), ChinaNitin Malhotra, Gobindgarh Public College, IndiaNuriadi Nuriadi, University of Mataram, IndonesiaOlena Andrushenko, Universität Augsburg, GermanyOmar (Mohammad-Ameen) Hazaymeh, Al-Balqa Applied University / Al-Huson University College, JordanOmsalma Ahmed, University of Hail, Saudi ArabiaÖzkanal, Ümit, Eskisehir Osmangazi University, TurkeySafi 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, IndiaWARID BIN MIHAT, Academy of Language Studies, MARA University of Technology (UiTM), MalaysiaZaldy Maglay Quines, Royal Commission for Jubail and Yanbu, Saudi Arabia
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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 | 0.435 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.109 | 0.076 |
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".