Making sense of organizational and occupational identities of management and professional staff at the University of British Columbia
Bibliographic record
Abstract
Universities underwent tremendous change and growth over the last decade triggering a rise in management and professional (M&P) staff. A combination of factors contributed to the changes including the implementation of corporate and new public management (NPM) strategies to manage and monitor specialized and growing areas in higher education. Some of these emerging areas included: internationalization; fundraising and development; community outreach; revenue generating venues; and, innovative teaching and learning initiatives. Universities began to rely increasingly on professional staff, with specialized expertise, to run institutional services and operations. The University of British Columbia (UBC), a large, publicly-funded, research-intensive university located in British Columbia, Canada, was no exception. In 2018, M&P staff numbered 4,530 members (AAPS, 2018), or 28% of the university’s reported workforce of 16,089 employees (UBC, Overview and Facts). M&P staff now makes up the single largest workforce sector at the university. This burgeoning group of highly-educated, skilled ‘new professionals’ (Gornall, 1999) is recognized by the university as a unique employee group through its professional organization, the Association of Administrative and Professional Staff (AAPS). However, despite their professional contributions to the operations and the strategic long-term mission of the university, in institutional literature these employees are bundled under the homogenizing term ‘staff’. This terminology clearly separates them from the ‘other’ employee group, academic faculty, and effectively positions them in a blurred, or ‘third space’ (Whitchurch, 2013), a conceptual place sandwiched between the unionized staff and faculty members. Through one-on-one interviews with 15 participants and document analysis, this research explored how some M&P staff members made sense of, and navigated, their occupational and organizational identities within that third space at the university. In addition, the study explores how participants coped with challenges around recognition, professional development, and high turnover. As the higher education sector continues to evolve and grow, the roles of M&P staff will also evolve and grow. This research contributes to understanding how M&P staff make sense of their positioning within the university, and in turn, how this workforce can be better supported in order to minimize the challenges they face and to promote a more productive work environment.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.032 | 0.011 |
| Scholarly communication | 0.020 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".