Carving Space for Staff Agency in a Faculty of Medicine: A Foucauldian-inspired Discourse Analysis of Administrative Staff and Faculty Relations
Bibliographic record
Abstract
Administrative staff in higher education have been described as invisible (Eveline, 2004; Szekeres, 2004) and often characterized as being “non-academic, non-faculty, non-teaching, [and] non-professional” (Losinger, 2015, p. 157). These characterizations manifest within health professions education (HPE) contributing to the undervaluing of staff and staff contributions. While administrative staff are present on campuses or within the virtual workspace, staff often remain absent when it comes to HPE documents, literature, and reports. With few exceptions, if staff appear in the HPE literature it is as a passive object, often as a resource, a possession, or a liability. If staff appear in institutional reports, it is often within an acknowledgement section rather than a list of authors. These absences are also felt in the everyday staff experience: staff are sometimes overlooked in meetings, may not feel comfortable contributing knowledge, and may feel devalued or invisible in their roles. At the same time, the neoliberal university system has led to the increasing professionalization of staff roles, occurring as health professionals experience their own shifts in power and prestige. These changes affect staff and faculty relationships in the health professions education space at times, leading to tensions if not toxicity. Throughout this thesis I build and examine an archive of published literature, archival documents, interview data, and my reflections and lived experience as a staff member to conduct a Foucauldian-inspired discourse analysis. Specifically, I conduct a “history of the present” (Foucault, 1977; Garland, 2014) to identify discourses that regulate the work of and power relations between administrative staff and faculty in a faculty of medicine. I discuss what these discourses make possible for staff to do, be, or say and what these discourses now make impossible. I explore the material effects of discourse, very purposefully centering staff voices within this text. Using feminist and decolonial critical theories throughout my analysis, I engage with the historic and hierarchical structure of academic medicine that constructs the largely feminized administrative staff cohort as having limited agency in HPE. To navigate the tensions produced by discourses and structure, I work to rebuild agency through staff voices, resistance, and recommendations for practice.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.028 | 0.079 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".