Expert in the spotlight in Oktober/November 2013 : Lydia Sciriha
Bibliographic record
Abstract
The expert in the Spotlight feature gives you the chance to interact one-on-one with our Ask the \nexpert-section. The feature also provides interesting and insightful comments regarding the subjects \nmentioned above, in-depth content and exclusive Q and A’s. Dr. Lydia Sciriha is Professor of Sociolinguistics in the Department of English at the University of \nMalta ( Linken naar http://www.um.edu.mt/). Her research interests are: Bilingualism, Sociolinguistics, \nDiscourse Analysis and Language Surveys. She is the author of the Regional Dossier: Maltese, the \nMaltese language in education in Malta, which will be published in October 2013 by Mercator \nResearch Centre. \nProfessor Sciriha has won a number of awards including the Canadian Commonwealth Scholarship, \nthe British Council scholarship, the Marquis Scicluna Senior Fellowship and the Commonwealth \nAcademic Fellowship. She has taught in Australia, Cyprus, Germany and Luxembourg. \nProfessor Lydia Sciriha is the author or co-author of eleven books and has edited two volumes of \nHumanitas Journal of the Faculty of Arts and number of articles in linguistics in scientific journals. \nMore biographical information at https://www.um.edu.mt/profile/lydiasciriha
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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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.300 | 0.173 |
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".