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 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 | 0.038 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.025 |
| Scholarly communication | 0.028 | 0.045 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".