HEI employer-engagement – a practitioner's reflections using institutional logics lenses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Higher Education Institutions (HEIs) are increasingly engaging with businesses to provide opportunities for their students whilst also promoting their own relevance and impact in society. However, this engagement often involves navigating multiple internal processes and practices. This paper presents an employer-engagement practitioner's reflections on HEI employer-engagement, using the lens of two institutional logics – professional and corporate logics. This paper considers how these logics are shaping HEI employer-engagement, the challenges for employer engagement middle managers (EE-MM), and tensions with senior leadership teams (SLT). This paper is framed and located from the point of view of a middle manager working within employer engagement, reflecting the challenges being faced by myself and seven other EE-MM across five English HEIs as part of my doctoral research. It explores the tensions between EE-MM and HEI-wide SLT; highlighting the competing demands between day-to-day management and strategic overview.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it