Data by Me, You, and Us: Exploring Data Transformation for Eco-Social Changes
Bibliographic record
Abstract
Plastic waste is one of the most pressing environmental issues, posing significant risks to human health and marine ecosystems. Although efforts to ban single-use plastics are increasing, plastic pollution continues to escalate. Citizen science projects have been used to address this challenge, inviting communities to help observe, record, and remove plastic from marine environments to support environmental solutions and influence policy. However, this research argues that these efforts fall short in fostering behaviour change among participants, as data remains primarily a resource for analysis rather than a catalyst for social transformation. Shifting this perspective, the study explores the potential of citizen-generated data to act as a medium that inspires change by capturing citizens’ reflections, emotions, and insights on plastic consumption and waste. By applying the concept of meshwork to understand how data is interwoven in daily life, the project proposes an embodied approach to tracking plastic consumption through a collectively designed data-tracking kit and co-creation workshop. Told through a series of infographics and analyses, the paper proposes a new perspective on data as an active force in addressing complex environmental challenges, such as plastic waste, through citizen involvement and experiential learning.
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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".