Bibliographic record
Abstract
The Islamic State in Iraq and Syria (ISIS) shocked the world when it entered the world stage in 2014. By seizing large territories in Syria and Iraq, its expansion within the Middle East seemed unrestrainable as the Iraqi Army fell apart and in Syria, various actors were caught up in a bloody civil war in the wake of the Arab spring. With US support, the Syrian and Iraqi Kurds became the main stand against ISIS acting as boots on the ground. The Kurdish success against ISIS in both Syria and Iraq quickly won them acclaim from the West and rendered them the key ally in the continued US-led effort against ISIS. In Western media and among Western decision makers, the Kurds as an entity are highly praised for their role in this fight. In addition, their fight for human rights, gender equality, secular rule, and democracy has also been highlighted as key features in assessing Kurds as a homogeneous entity that almost naturally shares strategic interest with the West. This book, as it sets out to paint a more comprehensive picture of the different Kurdish groups in Syria and Iraq, concludes that these groups indeed have very different policy objectives and diverging regional partners of alliance thereby questioning the underlying assumption of a uniformed Kurdish entity. The book makes a significant contribution to a better understanding of the political goals, affiliations and rivalries of these important actors and Western allies in the Middle East.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".