How do Danish humanities PhD school leaders constitute their roles? Interactions of biography, place and time
Bibliographic record
Abstract
Purpose Increasingly governmental policy around PhD education has resulted in greater university oversight of programs and student experience – often through creating central PhD Schools. While student experience is well researched, the experiences of Heads of these units, who are responsible for creating student experience, have been invisible. This exploratory Danish case study begins such a conversation: its purpose to examine the perceptions of five Heads of PhD Humanities Schools, each responsible for steering institutional decisions within Danish PhD policy landscapes. Design/methodology/approach A qualitative approach integrated three distinct analyses: a review of Danish PhD education policies and university procedures, each university’s job specifications for the Heads of the Schools and the Heads’ views on their responsibilities. Findings The Heads differentiated between their own and today’s PhD student experience. They had held prior leadership roles and fully supported institutional regulations. They cared deeply for the students under their charge and were working to achieve personal goals to enhance PhD experience. Their leadership perspective was relational: enhancing individual student learning through engaging with multiple PhD actors (e.g. program leaders) – when possible at a personal level – to improve PhD practices. Originality/value This study contributes an expanded perspective on how PhD School Heads constitute their roles by empirically linking: macro-national policies and institutional regulations and individuals’ biographies to their support of the PhD regimes – with implications for academic leadership generally. The authors argue research into PhD School leadership is essential, as it is such individuals who create the organisational settings that students experience.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".