Cultivating consent: frontier-making and the politics of smallholder subordination in western Turkey’s sweet cherry frontier
Bibliographic record
Abstract
Abstract The rise of neoliberal statism in Turkey, where the state acts as a developer in both urban and rural contexts, illuminates the multi-scalar, negotiated, and power-laden nature of frontier-making in the twenty-first century. The expansion of export-oriented sweet cherry production in western Turkey’s peri-urban landscapes exemplifies the uneven, non-linear, and contested trajectories of contemporary agrarian capitalism. This paper examines how a niche-commodity frontier is produced not only through shifts in political–economic and socio-ecological relations of production, but also through articulations of nationalism and moral authority grounded in religion. Among the various actors involved in this process, smallholders occupy a paradoxical position: structurally subordinated within export value chains yet discursively mobilized as key agents of frontier expansion. Drawing on ethnographic fieldwork, the paper argues that the ideological hegemony underpinning the frontier hinders the formation of smallholder class consciousness. Instead, the articulation of agrarian capitalism with nationalist and developmental imaginaries, expressed through the party politics of the ruling Justice and Development Party (AKP), secures widespread allegiance to Turkey’s current iteration of neoliberal development.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".