Valuing humanities: Rethinking the humanities-impact landscape in Denmark
Bibliographic record
Abstract
Globally, the issue of research impact has grown as governments articulate policies around research as a contributor to economic and societal development, often through an econometric justification. This has triggered much discussion amongst humanities scholars in public formally-reasoned peer-reviewed texts that are rarely empirically-based. This Denmark-based empirical study used an individual biographical and historical structural framework to explore how humanities academics in face-to-face semi-formal interactive interviews viewed this issue. The results highlighted a nuanced understanding of what we call the humanities-impact landscape, with three potential interactions falling along a continuum suggesting further inquiry is warranted. The study contributes a rich tapestry of the interwoven individual and structural elements at play when academics articulate how they locate themselves within the landscape, ones that might not be seen in more conceptual arguments.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.029 | 0.011 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".