Curriculum Politics in Higher Education: What Educators need to do to Survive
Bibliographic record
Abstract
Higher education institutions are increasingly experiencing pressure regarding their expected role in addressing immediate and long-term sustainable development challenges. Decisions about what should be taught are heavily influenced by socio-political needs and aspirations. The push towards entrepreneurship education is, perhaps, one example where some governments expect higher education institutions to encourage entrepreneurial development and awareness among students of the institutions. Indeed, political action has become a well-known force in education systems throughout the world. Utilizing a conceptual approach, this paper examines the theory and practice of curriculum politics in the Trinidad and Tobago higher education sector. It also explores various ways in which educators can survive the perceived threat of political interference in curriculum decision making. Notwithstanding the role of politics in curriculum decision making, the paper supports the view that unrestrained political intervention from non-education sources may threaten the quality of higher education programmes. As such, educators must come to terms with the reality of curriculum politics and find ways to function optimally in any given political context.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".