Open Education: Looking at Canadian Higher Education through a Critical Research Lens
Bibliographic record
Abstract
Open education (OE), as the sharing, use, and reuse of resources, pedagogies, and teaching practices, is an evolving phenomenon globally. OE has gained momentum by challenging, transforming, and even displacing systems which exclude, disenfranchise, and marginalize members of both the public and academic communities. Traditional, dominant systems are problematic because they create barriers that restrict access, agency, ownership, participation, and experience. OE approaches represent a wide range of solutions from free open educational resources to open access of scholarly research. A complex open and closed ecosystem, coupled with flaws and weaknesses in OE practices and approaches themselves, create issues and tensions needing closer interrogation. This paper provides a brief literature review on OE, with an emphasis on how meaning has evolved from being content focused to practice focused, alongside with the progression in an aim towards social justice and equity. A look at how OE is constituted within international and Canadian policy discourse also informs how conceptualizations form under social and political contexts. It is argued that critical theoretical frameworks can interrogate the OE phenomenon, particularly within Canadian higher education. A critical research lens can be beneficial in providing understandings of power relations as they affect social justice and equity.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.042 | 0.070 |
| Scholarly communication | 0.031 | 0.013 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".