Collaborative Research in the Datafied Society
Bibliographic record
Abstract
The influence of austerity measures and neoliberal ideologies has sparked discussions about the relevance and value of academic institutions, particularly in the humanities and social sciences. Universities are redirecting academic focus towards greater societal engagement. This book argues that academia has much to gain by moving beyond its institutional walls, in our case, by doing data work with stakeholders and civil society. This collaborative work benefits citizens in our democratic, open societies and advances our knowledge economies. Collaborative Research in the Datafied Society offers a combination of theoretical insights, practical methodologies, and case studies, showcasing the power of collaborative research with stakeholders across diverse communities and civil society to tackle challenges that address pressing issues stemming from data practices and social justice issues. Taken together, the book’s chapters formulate relevant concepts for grounding societally engaged research in the theories and methodologies from different disciplines. In addition, the book informs university administrators and research directors how to advance academia effectively towards mutual knowledge transfer with societal sectors.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".