Big Data as a Tool to Monitor and Deter Environmental Offenders in the Global South: A Multiple Case Study
Bibliographic record
Abstract
While prior research has looked at big data’s role in strengthening the environmental justice movement, scholars rarely examine the contexts, mechanisms and processes associated with the use of big data in monitoring and deterring environmental offenders, especially in the Global South. As such, this research aims to substitute for this academic gap through the use of multiple case studies of environmental offenders’ engagement in illegal deforestation, as well as legal deforestation followed by fire. Specifically, we have chosen four cases from three economies in the Global South: Indonesia, Peru and Brazil. We demonstrate how the data utilized by environmental activists in these four cases qualify as true forms of big data, as they have searched and aggregated data from various sources and employed them to achieve their goals. The article shows how big data from various sources, mainly from satellite imagery, can help discern the true extent of environmental destruction caused by various offenders and present convincing evidence. The article also discusses how a rich satellite imagery archive is suitable for analyzing chronological events in order to establish a cause-effect chain. In all of the cases studied, such evidentiary provisions have been used by environmental activists to oblige policy makers to take necessary actions to counter environmental offenses.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".