“Men who love the oak trees”: services and care in the cork oak forests of Southern Andalusia
Bibliographic record
Abstract
Our article is aligned with the growing interest from the sustainability science in the need to broaden our view of the connections between humans and nature for better understanding them that helps to improve environmental governance systems. Our work aims to provide evidence that helps to overcome the dualistic and utilitarian prejudices that the ecosystem services framework presents on this issue, hindering its potential, both theoretically and practically. To this end, it is essential to incorporate the affective dimension that often permeates this relationship and turns it into care. Moreover, we do this in a social-ecological context such as that of the European Mediterranean, which is different from that of the non-western indigenous populations, of which we have examples that demonstrate the proactive role that people have played and continue to play in the construction and conservation of valuable ecosystems. We take as a case study the activities carried out by the people who work in the cork oak forests of southern Andalusia for their maintenance and for the extraction of cork. Drawing on the knowledge gained from a long period of mainly ethnographic re-search, we advocate incorporating the care practices, knowledge, and feelings that permeate the relationships that these workers have with the trees and the forest into the governance spaces of these landscapes as fundamental elements to achieve the sustainable management of cork oak forests. This is particularly relevant in a context marked by the decline in the socio-environmental conditions of these agroforestry social-ecological systems due to ageing of the trees and the diseases affecting them as a result of changes in use and management, aggravated by climate change.
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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 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".