The need for protection of environmental defenders from digital intimidation: an analysis of Article 3(8) of the Aarhus Convention
Bibliographic record
Abstract
Digital technologies are becoming increasingly important to environmental defenders,both in terms of tools that facilitate speaking out and/or taking action, and in termsof (digital) risks they face as a result of their involvement. A growing concern has beenexpressed about the use of various forms of online and technology-facilitated intimidationor “digital intimidation” against environmental defenders. While the existing research oncyberbullying, digital violence and online intimidation can provide some insight, few studiesand data exist on the use of such tactics against environmental defenders in particular.By leaving this issue unexamined, there remains a lack of awareness about the risks andchallenges environmental defenders may face in terms of online safety and digital intimidation,which may ultimately curtail public debate on environmental issues. Fortunately, theprotections under Article 3(8) of the Aarhus Convention and the recently introduced SpecialRapporteur for Environmental Defenders can be useful in providing protection againstdigital intimidation. This paper, therefore, looks at the application of Article 3(8) to digitalintimidation, through the decisions of the Aarhus Convention Compliance Committee, andalso considers the mandate given to the Special Rapporteur at the 2020 Meeting of theParties. The analysis shows that there is certainly potential for protection against digital intimidationunder Article 3(8) AC, but more explicit attention and awareness may be needed.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".