Book Reviews
Bibliographic record
Abstract
Hamilton, Sarah R. 2018.Cultivating Nature: The Conservation of a Valencian Working Landscape. Seattle: University of Washington Press. 312 pp. ISBN 978-0-295-74331-8. Besky, Sarah, and Alex Blanchette, eds. 2019.How Nature Works: Rethinking Labor on a Troubled Planet. Albuquerque: University of New Mexico Press. 272 pp. ISBN: 978-0-8263-6085-4. Lora-Wainwright, Anna. 2017.Resigned Activism: Living with Pollution in Rural China. Cambridge, MA: MIT Press. 272 pp. ISBN: 978-0-2620-3632-0. Symons, Jonathan. 2019.Ecomodernism: Technology, Politics and the Climate Crisis. Cambridge: Polity. 232 pp. ISBN: 978-1-5095-3120-2. Miller, Theresa L. 2019.Plant Kin: A Multispecies Ethnography in Indigenous Brazil. Austin: University of Texas Press. 328 pp. ISBN 978-1-4773-1740-2. Aistara, Guntra. 2018.Organic Sovereignties: Struggles Over Farming in an Age of Free Trade. Seattle: University of Washington Press. 272 pp. ISBN 978-0-295-74311-0. Drew, Georgina. 2017.River Dialogues: Hindu Faith and the Political Ecology of Dams on the Sacred Ganga. Tucson: University of Arizona Press. 264 pp. ISBN: 978-0-8165-4098-3. Folch, Christine. 2019.Hydropolitics: The Itaipú Dam, Sovereignty, and the Engineering of Modern South America. Princeton: Princeton University Press. 272 pp. ISBN: 978-0-6911-8659-7.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.512 | 0.439 |
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".