The Security Gap in Syria: Individual and Collective Security in ‘Rebel-held’ Territories
Bibliographic record
Abstract
This paper examines security in Syria through the conceptual lens of the security gap, understood as the gap between security practices and objectives which have implications for individual and collective security. Practices of security can be the state apparatus, the military, and militias. The objective – safety – can refer to the safety or security of a range of collectives including the state, political parties, and ethnic groups, while individual security refers to the general safety of inhabitants and the protection of human rights. This paper compares the security situation in so-called ‘rebel-held’ areas of Syria where alternative governance structures have emerged, examining the security approaches of Local Administrative Councils and Rebel Councils in Deir Azzor, Manbij, Dera, and areas dominated by the Kurdish Democratic Union Party (PYD). It argues that security and safety are strongly influenced by authority formation and the nature of deals and relationships involved in the formation of these nascent authorities. It also argues that security in these areas is strongly influenced by the Syrian government, which disrupts collectives that threaten its own collective security while giving limited support to those which serve its agenda of retaining power. It also demonstrates the limited utility of the ‘regime’ vs. ‘rebel-held’ dichotomy, as rebel groups at times must accommodate the Syrian state in limited ways for instrumental purposes. The article is based on fieldwork conducted in Turkey in 2013–2014, interviews conducted in 2015, and secondary sources based on field research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".