Leveraging data science for change: navigating perspectives in a world of rapid transformation.
Bibliographic record
Abstract
In today’s interconnected world, data surged in volume. This exponential increase in data availability has sparked the rise of data science and artificial intelligence (AI), changing how we handle information (Aldoseri et al. 2023). In a world undergoing rapid change across various dimensions, it is important to involve data-driven methods to be able to assess and to follow, and assist citizens in their sustainable actions and projects. These methods help analyze, support, and motivate citizens in actions and projects, addressing complex issues like biodiversity loss, urban liveability, and local activism. With a focus on inclusivity and adaptability, the session aimed to discuss transformative potential approaches that integrate citizen science, data science, and human-computer interaction (HCI). Of particular importance was the focus on the ethical considerations of data science and AI, emphasizing fairness and equity in decision-making, and showcase real-world impacts. Additionally, we explored the conditions for cross-disciplinary collaborations among data scientists, citizen scientists, researchers, practitioners, within various citizen science projects. The current paper presents the session report, highlighting the main discussion points and conclusions.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.011 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".