Bigger Data and Quantitative Methods in the Study of Socio-Environmental Conflicts
Bibliographic record
Abstract
New data sources that I characterize as “bigger data” can offer insight into the causes and consequences of socio-environmental conflicts, especially in the mining and extractive sectors, improving the accuracy and generalizability of findings. This article considers several contemporary methods for generating, compiling, and structuring data including geographic information system (GIS) data, and protest event analysis (PEA). Methodologies based on the use of bigger data and quantitative methods can complement, challenge, and even substitute for findings from the qualitative literature. A review of the literature shows that a particularly promising approach is to combine multiple sources of data to analyze complex problems. Moreover, such approaches permit the researcher to conduct methodologically rigorous desk-based research that is suited to areas with difficult field conditions or restricted access, and is especially relevant in a pandemic and post-pandemic context in which the ability to conduct field research is constrained.
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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 | 0.162 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".