Collectivity in data governance and data justice
Bibliographic record
Abstract
With this special issue, we seek to critically reflect on contemporary debates on data governance and data justice by foregrounding the question of collectivity from different perspectives. The issue brings together scholars from across the globe to consider how the collective features within ideas of data governance and justice, and what responses to concerns about datafication might look like beyond the individualist paradigm. It builds on the successful third Data Justice conference held in Cardiff in June 2023 on the theme of Collective Experiences in the Datafied Society hosted by the Data Justice Lab. This conference brought together close to 300 participants from around the world to explore the different ways collectives shape and are impacted by datafication today. The papers in this special issue assemble some of the outstanding contributions at the conference.
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 | 0.026 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.034 | 0.028 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.012 | 0.027 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".