Social-Ecologies of Crisis: Assessing the Back-to-Land Movement in Greece
Bibliographic record
Abstract
abstract: Adaptation and transformation have emerged as a key themes for human-environment research, especially in the context of rapid social-ecological changes. The 2008 global financial crisis constitutes a major driver of change with social-ecological ramifications that have yet to be fully explored. Using Greece, the poster child of the euro-crisis as a case-study, this dissertation examines how adaptive capacity is mobilized and even enhanced in times of crisis, paying particular attention to the role played by natural capital. To do so, I focus on the back-to-land trend whereby urbanites seek to engage in food production post-crisis (2008-onwards). In-depth qualitative analysis of back-to-landers’ motivations, experiences, and challenges is integrated with quantitative data about household demographics, incomes and assets, and land management characteristics. The dissertation is organized in three main result papers (chapters). The first seeks to understand why people turn to the land in times of crisis, and the role played by agency. The second analyzes the various assets that people mobilize in order to go back to the land, paying particular attention to the different mobilities necessary for their livelihood transformation. The third examines environmental safety nets in terms of material and non-material benefits that ecosystems provide to people. This research contributes to a wider social-ecological scholarship that seeks to understand how people adapt and transform when confronted with crises, focusing on how land and associated ecosystem services contribute to the resilience of these households, and the role played by agency in this process.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".