Collaborative Research in the Datafied Society
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.011 | 0.009 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it