Decolonizing Digital Citizen Science: Applying the Bridge Framework for Climate Change Preparedness and Adaptation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it