The Challenge of Addressing Subjectivities through Participatory Action Research on Datafication
Bibliographic record
Abstract
Participatory action research leans heavily on the subjectivity of knowledge producers as a vehicle for change. In our work, however, we encountered several assumptions about how subjectivities, including our own, can be drawn on to address datafication’s implications for data subjects. We argue that participatory data literacy interventions, if they are to create fundamental change in the face of datafication, cannot be technocentric or top-down instruments. Rather, they must depart from the processes of subjectivation taking place in relation to specific instances of datafication. In addition, data subjects must have the tools necessary to engage in critical reflection so that they recognize collective subjectivities and/ or embrace their individual agentic capacity. Without these tools, it is difficult to realize participatory transformations.
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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 | 0.273 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.152 |
| Scholarly communication | 0.031 | 0.054 |
| Open science | 0.009 | 0.033 |
| Research integrity | 0.009 | 0.027 |
| Insufficient payload (model declined to judge) | 0.007 | 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".