Citizen science and the right to research: building local knowledge of climate change impacts
Bibliographic record
Abstract
Abstract The article presents results of a research project aiming to develop theoretical and empirical contributions on participatory approaches and methods of citizen science for risk mapping and adaptation to climate change. In the first part, the paper presents a review of the literature on key concepts and perspectives related to participatory citizen science, introducing the concept of the “right to research”. It highlights the mutual fertilization with participatory mapping methods to deal with disaster situations associated to climate change. In the second part, the paper describes and presents the results and conclusions of an action-research developed on the coastline between the states of São Paulo and Rio de Janeiro, Brazil in 2017–2018. It involved affected communities as protagonists in mapping and managing risks of natural disasters caused by extreme climate events, by combining citizen science approaches and methods with Participatory Geographic Information Systems (PGIS) and social cartography. The article concludes by pointing out the contributions and limits of the “right to research” as a relevant Social Science approach to reframe citizen science from a democratic view.
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.
How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".