Making a Difference : The Epistemic Value of Collaborative Research in a Datafied Society
Bibliographic record
Abstract
This chapter addresses the evolving role of academia amidst budget constraints and neoliberal policies, highlighting the growing need for its work to be more socially relevant, especially in the humanities. It argues that academia can actually benefit from moving beyond its institutional walls, engaging with diverse community and civil society stakeholders. Such collaboration enables universities to respond to pressing societal challenges. The chapter explores three primary motivations for increased academic engagement with societal sectors, identified by researchers and university administrators: vocational, educational, and societal impetus, and advocates for a fourth motivation: the epistemic impetus. Collaborative research allows researchers to gather evidence and generate insights to produce knowledge with communities and in context, enriching academic research and allowing interventions and the application of findings.
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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 | 0.029 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.073 |
| Scholarly communication | 0.028 | 0.043 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".