‘We live and die in chestnut’: remaining and adapting in the face of pest and disease outbreak in Turkey
Bibliographic record
Abstract
Concern is growing worldwide over the negative outcomes of rural abandonment. Yet, problematisation of this phenomenon remains limited by insufficient explanatory frameworks and lack of empirical evidence from the conditions which precede, underlie and succeed it. Accordingly, this paper presents a case from Turkey, where significant rural abandonment is locally attributed to the ravages of multiple introduced pathogens in European chestnut (Castanea sativa) populations, and where our previous investigation has verified that traditional livelihood practices mitigate damage severity at the levels of trees, plots and landscapes. In order to better understand individual stakeholder motivations for remaining acting members of chestnut landscapes in the face of such serious challenges, we conducted 142 extended ethnographic and narrative interviews with chestnut-utilising participants across Turkey’s highly diverse human and physical geography. Our results show how the struggle to remain as acting landscape members requires community livelihood adaptation, drawing on institutional memory, innovative learning and social connectedness.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".