Decolonizing Digital Citizen Science: Applying the Bridge Framework for Climate Change Preparedness and Adaptation
Bibliographic record
Abstract
Research has historically exploited Indigenous communities, particularly in the medical and health sciences, due to the dominance of discriminatory colonial systems. In many regions across Canada and worldwide, historical and continued injustices have worsened health among Indigenous Peoples. Global health crises such as climate change are most adversely impacting Indigenous communities, as their strong connection to the land means that even subtle changes in the environment can disproportionately affect local food and health systems. As we explore strategies for climate change preparedness and adaptation, Indigenous Peoples have a wealth of Traditional Knowledge to tackle specific climate and related health issues. If combined with digital citizen science, data collection by citizens within a community could provide relevant and timely information about specific jurisdictions. Digital devices such as smartphones, which have widespread ownership, can enable equitable participation in citizen science projects to obtain big data for mitigating and managing climate change impacts. Informed by a Two-Eyed Seeing approach, a decolonized lens to digital citizen science can advance climate change adaptation and preparedness efforts. This paper describes the ‘Bridge Framework’ for decolonizing digital citizen science using a case study with a subarctic Indigenous community in Saskatchewan, Canada.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.040 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.005 | 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".