Extractivism, Neoliberalism, and the Environment: Revisiting the Syrian Conflict from an Ecological Justice Perspective
Bibliographic record
Abstract
The ongoing Syrian conflict has triggered one of the worst humanitarian emergencies and the largest refugee crisis of the post-World War II era. What are the political-ecological factors leading to the emergence and spread of this conflict? To what extent have ecological injustices played a triggering or accelerating role in the Syrian conflict? The main argument of this article is that one of the most important causes of the Syrian tragedy relates to the outbreak of a political-ecological crisis whose origins are to be found in the long-term consequences of Syria's (a) oil-centered extractivist model of development adopted since the 1970s and its legacy reflected in the government's failure to generate adequate livelihood; (b) neo-liberal restructuring that has widened inequalities and bankrupted the agriculture since 2000; and (c) environmentally blind policies that have neglected the severity of droughts, encouraged water-intensive crops and the over-exploitation of water resources, and failed to address the modernization of the irrigation infrastructure. The environmental aspects of this crisis also concern inter-state and political-cultural conflicts as they apply to the control of Syria's water resources by Turkey and the so-called Islamic State.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".