Centre for Contemporary Middle East Studies: A Multi-Disciplinary Approach
Bibliographic record
Abstract
In lieu of an abstract, this is the article's first page:In May 2007, the board of University of Southern Denmark (SDU) made a strategic decision in declaring the field of modern Middle East studies a priority research area of the Faculty of Humanities at SDU. The board underpinned this decision with the allocation of substantial additional means to the Centre for Contemporary Middle East Studies at SDU (hereafter “the Centre”). The research staff of the Centre was augmented by two regular professorships, one guest professorship (one-year term) and two PhD positions. In this way, the board aimed at strengthening the research component of the Centre and its international profile. The first new faculty member was guest professor Francesco Cavatorta (today teaching at Université Laval in Canada), who was employed from August 2008 to July 2009. Since then, nine scholars have served as guest professors at the Centre, representing countries as diverse as Germany, India, Ireland, Jordan, Turkey, and the United States. In January 2009, the Faculty of Humanities appointed Dietrich Jung as the first regular professor. Jung previously worked as a senior researcher at the Danish Institute for International Studies (DIIS). He also took over the directorship of the Centre from Associate Professor Peter Seeberg who had served in this position since (...)
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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 | 0.018 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.018 | 0.030 |
| Scholarly communication | 0.029 | 0.013 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".