SWFS - Governing land and landscapes: Political ecology of enclosures and commons
Bibliographic record
Abstract
Most of the world’s food is still produced by small farmers, many of whom remain organized though customary land tenure. Customary tenure is a general term for specific cultural ways in which farmers embedded in ecological contexts allocate rights and obligations to use land, including cultivation, forest, grazing, and water. These are always unique, but they share the quality of not being centrally based on the kinds of land markets created in so-called advanced economies. An important feature at the present moment is direct appropriation of land and conversion of customary land use into private titles to specific plots of land. These include major deals with national governments in Africa and throughout the world to make huge areas of land (or water necessary to use the land) available to national elites, foreign governments, or large corporations. They also include international aid policies which, in trying to encourage small farmers to participate more directly in world markets, encourage a shift to individual titles. These actions threaten to dissolve the capacity of communities to govern the land as social and ecological conditions change (Tran, Provost, & Ford, 2014).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.048 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".