Unveiling university-society engagement – university origin stories from Denmark
Bibliographic record
Abstract
While contemporary higher education policy tends to frame the value and contribution of the university through the concept of societal impact, in this paper, we aim to widen how we understand engagement between the university and society beyond the narrow boundaries of ‘impact’. The broad challenges in demonstrating societal impact of universities, especially experienced by humanities disciplines, underscore a conceptual mismatch between the value of universities and their evaluation, and suggest that a different theorisation of the value of the university to society is worth deliberating. In this paper, we draw from Danish university origin stories to unveil the meso context in which Danish universities operate alongside other societal institutions and actors. By considering who these actors may be, what their contributions were, and why they supported the establishment of universities, we bring to light a hidden and more nuanced aspect of university-society engagement – one that is dynamic, reciprocal, and conceptually embedded into what the university is as an actor within society and the world. Such a historically-informed understanding of university-society engagement offers a richer, more complex approach which may apply to alternative future policy framings, to more accurately capture the significance of the university to society and vice versa.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.019 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".