Past, Present, and Future of Language Assessment: An Interview with Dr. Hossein Farhady
Bibliographic record
Abstract
Professor Hossein Farhady is an outstanding English Language Teaching (ELT) scholar in the field of applied linguistics, in general, and aspects of language testing and assessment, in particular with the English as a Foreign Language (EFL) and English as a Second Language (ESL). He has been teaching various courses on language testing and assessment, research methods, and English for Specific Purposes (ESP) at MA and Ph.D. programs for the last four decades in Iran, the USA, Canada, Armenia, and Turkey. He has also been a prolific writer, publishing papers and books in prestigious international journals and publishing houses and presented numerous speeches and papers at national and international seminars and conferences. Moreover, he has supervised more than 80 MA theses and Ph.D. dissertations. His widely used book about research methodology for applied linguistics commonly known as “Hatch and Farhady” (1982) has served as the basic textbook at both undergraduate and graduate levels since the early 1980s. He has, additionally, been a curriculum developer and test developer in Iran and other parts of the world. He worked as a senior scholar and project manager for several organizations such as Ordinate and Lidget Green Corporations in California, Avant Assessment, and Second Language Testing, INC., in Rockville, Maryland. He has also received research grants and awards from organizations such as Pearson, Educational Testing Service (ETS), and International Language Testing Association (ILTA) in scoring and validating language tests. Currently, he is a faculty member at the English Language Teaching (ELT) Department at Yeditepe University in Istanbul, Turkey.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".