The impact of higher education professionals on university structure
Bibliographic record
Abstract
With technological innovations, financial constraints in the education sector, and fewer academic positions available to candidates with various educational qualifications, higher education is going through a major change. Increasing numbers of qualified individuals with graduate degrees find their jobs in academic support positions straddling both the academic and administrative domains. This growing cadre of professionals within a university context faces different challenges in terms of what and how they do the work for the university. The study explores the emergence of higher education professionals (HEPROs), specifically at Teaching and Learning Centres within the Canadian higher education landscape and their impact to the structure of the university. In higher education, the logic of managerialism is competing with the professional logic of academia. It is HEPROs that are filling the gap within the organizational configuration of these two competing logics in Canadian post-secondary education. Using a phenomenological approach, this qualitative inquiry was focused on the experiences of higher education professionals bounded within the context of Teaching and Learning Centres in universities. Findings from this study revealed the challenges that many HEPROs in leadership at Canadian teaching and learning centres encounter as they deal with the tensions that exist in their often hybrid roles – as a professional and management. These findings can inform universities on how to best situate HEPROs and capitalize on their qualifications and abilities to support the operations and structure of our institutions.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".