Bibliographic record
Abstract
This book, Environmental Social Sciences , represents the best of what’s happening in social science right now: (1) it exemplifies the movement toward interdisciplinary research; (2) it rejects the pernicious distinction between qualitative and quantitative in the conduct of social research; and (3) it makes clear the value for all social scientists of training in a wide range of methods of collecting and analyzing data. I treat these in turn. 1. Interdisciplinary social science. Environmental science has always been an interdisciplinary effort. The Science Citation Index lists 163 journals in the category of environmental science. Look through the top 10 journals (the ones with an impact factor of 4.0 or more) and the range of disciplines is clear: biologists, chemists, meteorologists, paleontologists, geologists … Increasingly, it is common to see articles – like one by Clougherty (2010) on gender analysis in the distribution of the effects of air pollution, or one by Knoke et al . (2009) on reconciling the subsistence needs of farmers in Ecuador with the need for conserving forests, or one by Rosas-Rosas and Valdez (2010) on the impact of fees from deer hunts on the willingness of landowners in Mexico to suspend killing of pumas and jaguars – articles that can only be described as social science. (We see this as well in medical science, where the very best journals now also routinely publish articles that also can only be described as 100% social science.)
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.551 | 0.490 |
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".