Scholarship reimagined: creating the DARSHE, an inclusive and flexible framework for developing scholarship in higher education
Bibliographic record
Abstract
Abstract The expansion and marketisation of Higher Education (HE) over recent years has prompted significant growth in the number and proportion of education-focused academics (EFAs) within the academy. Recognition and reward for these staff has however been a persistent challenge, in part because of debates around the nature of scholarship, variation in institutional criteria pertaining to scholarship, and lack of clarity around recognition of the work undertaken by EFAs. As part of our remit to develop scholarship in our own institution, we conducted workshops with academics from six departments within a post-1992 English university, capturing lived experiences of scholarship, understandings of what it comprised, and perspectives on how scholarship could be supported. We reflect on these discussions, on the literature defining scholarship, and on our own lived experiences as EFAs and supporting EFAs, to reimagine scholarship through an integrated framework, the DARSHE (Description of Activities Relating to Scholarship in Higher Education). The DARSHE framework offers a synthesis of some of the different approaches in the current literature, taking into account the development needs we have identified, and subsequent evaluations with wider stakeholders. The framework can be used by individuals to reflect on their scholarship, as a tool to support the development of an inclusive approach to scholarship by academic developers, and to enable reward and recognition for EFAs in university policy. We suggest that this will lead to a positive impact on colleagues seeking to develop their scholarship, the HE sector, and the students that study within it.
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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 | 0.122 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.020 | 0.080 |
| Scholarly communication | 0.038 | 0.045 |
| Open science | 0.007 | 0.057 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".