Bibliographic record
Abstract
Dear Reader,We, as the Editorial Team of the ELT Research Journal, are happy toannounce the release of the third issue of 2019 volume. In this issue, there arethree research articles addressing a number of issues in the field of EnglishLanguage Teaching.The first article by Erhan GÜLŞEN and Enisa MEDE aims to determine theeffects of online differentiated reading on comprehension skills and learnerthe autonomy of young learners at a private college in İstanbul, Turkey. Collecting thedata through pre-post-tests of reading comprehension and learner autonomy, semistructured student interview and reflective teacher journals, the study concludesthat integrating online differentiated reading lessons could be regarded as anthe efficient way of instruction to support the development of reading comprehensionskills and enhance the achievement of autonomy in foreign language classrooms.The second paper by Adnan DEMİR aims to examine the frequency of errorcorrection, types of negative feedback used, and learner involvement in errorcorrection procedures in three EFL class sessions in a university setting with threedifferent teachers. Embracing a qualitative research design, the study presents reallife instances from the language classroom under scrutiny. The study concludeswith the negative and positive impacts of feedback in language classrooms.Lastly, the third study of the issue by Tuba ÖZTURAN, Gülşah UYAR andAycan DEMİR AYAZ aims to compare the success of the immigrant students inreading skills in both countries as well as their sense of belonging and theirparents’ education by utilizing PISA-2015 data. The results display that theimmigrant students in Canada have outperformed their peers in Belgium.We would like to thank once more all the researchers who have contributedto the current issue of the journal with their invaluable academic works. We wouldalso like to thank all editors, co-editors and reviewers of the ELT-RJ for theirvoluntary contribution to the journal by managing the review process.Best Regards,Assoc. Prof. Dr.Gonca YANGIN EKŞİAssociate EditorAvailable online at:http://dergipark.ulakbim.gov.tr/eltrj/International Association of Researchin Foreign Language Education and Applied LinguisticsELT Research Journal2019, 8(3), 126ISSN: 2146-9814Prof. Dr. Gonca YANGIN EKŞİAssociate EditorProf. Dr.Dinçay KÖKSALEditor-in-Chief
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.167 | 0.114 |
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".