Guest Editor’s Introduction: Homa Katouzian, Exceptional Editor of <i>Iranian Studies</i>
Bibliographic record
Abstract
This special issue is dedicated to Dr. Homa Katouzian for his exceptional service as the Editor of Iranian Studies since 2004.With an unrivaled commitment to the advancement of this Journal of the International Society for Iranian Studies, during his editorship Katouzian transformed Iranian Studies from an irregular quarterly to a highly reputable and punctually published bimonthly.From the outset of his term, Katouzian was committed to opening the pages of the journal to a younger generation of scholars and actively engaged them in all aspects of the editorial process.With this editorial vision, he transformed the journal into an invaluable lever for the academic placement and advancement of younger scholars during an unprecedented period of shrinking resources for Iranian Studies.Concurrently, he mentored a new crop of talented younger editors who learned the complexity of journal publishing and how to thrive during the critical transition from paper to digital printing.Katouzian vigorously led the increased frequency of Iranian Studies from a quarterly to a bimonthly.Whereas before 2004 our quarterly journal was often released late and as double issues, he advocated publishing it more frequently and on schedule in single issues.Katouzian's argument was based on a sophisticated but unconventional reasoning.Some colleagues could not understand his rationale.Contra Katouzian, they argued that increased frequency would undoubtedly compromise the quality of the journal.With the rapid expansion of Iranian Studies as illustrated during the biennial conferences of the Society, however, Katouzian expounded that increased frequency would lead to the submission of more original articles by scholars working all over the world.He further contended that, with serious editorial supervision and peer-review, higher submissions would contribute to a higher quality of published scholarship.As illustrated in the published issues of the journal under his editorial supervision, Katouzian was right.Released on time as a quarterly in 2004, 2005 and 2006, between 2007 and 2010 the frequency of the journal was increased to five issues per year.Since 2011, the journal has been released punctually every two months.With this increased frequency,
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".