Bibliographic record
Abstract
Governments in western liberal economies, such as Ontario, are shifting to outcomes-based systems that ‘tune’ higher education curriculum to stakeholder interests. Globally, governments are using learning outcomes for quality assurance, to modernize curriculum for societal interests, and to apply government and market influence upon the curriculum. This thesis applies new institutionalism and the capability approach to examine how humanities leaders in Ontario higher education perceive and react to the new administrative layer of learning outcomes. According to institutional theory, responses may include superficial strategies for compliance, non-compliance, or the layering of new policies among existing traditions. The research asks, “How do Ontario university leaders in the humanities perceive and implement the shift to learning outcomes?” The study aims to understand to what extent an outcomes-based system aligns with the curricular priorities of the humanities in higher education. Narratives of 19 university humanities leaders were analyzed through qualitative interviews within 10 Ontario universities. The data was reviewed using thematic analysis, inductive and deductive analysis, and compared within the context of the outcomes-based education literature. The overarching narrative of participants indicated that learning outcomes were both an innovative opportunity for pedagogical reflection and a new burdensome administrative layer. The process of tuning the humanities curriculum with administrative pursuits, targeted government funding, and specified career outcomes was not widely accepted by the participants in the study. The approach of passive compliance with learning outcomes became more evident when asking about the consistency, and verifiability of outcomes achieved. Participants shared political challenges and chronological alignments with program restructuring, displaced curriculum, and in some cases program cancellations. Participants said that students were generally unfamiliar with learning outcomes. When discussing opportunities of learning outcomes, participants said that they were an effective discussion tool to envision curricular strategies for enrolment management, experiential learning, interdisciplinary programs, and large elective courses. The study adds to the literature by providing an in-depth view of the agency of department heads to manage the curriculum in the humanities, an understanding of the implementation of Ontario’s learning outcomes policy, and the political positioning of the humanities in Ontario’s higher education system. Key Words: Learning Outcomes, Ontario, Higher Education, New Institutionalism, New Public Management, Neoliberalism, Humanities, Capability Approach, High Participation System.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".