Institutional Culture and OER Policy: How Structure, Culture, and Agency Mediate OER Policy Potential in South African Universities
Why this work is in the frame
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Bibliographic record
Abstract
<p>Several scholars and organizations suggest that institutional policy is a key enabling factor for academics to contribute their teaching materials as open educational resources (OER). But given the diversity of institutions comprising the higher education sector—and the administrative and financial challenges facing many institutions in the Global South—it is not always clear which type of policy would work best in a given context. Some policies might act simply as a “hygienic” factor (a necessary but not sufficient variable in promoting OER activity) while others might act as a “motivating” factor (incentivizing OER activity either among individual academics or the institution as a whole).</p><p class="1">In this paper, we argue that the key determination in whether a policy acts as a hygienic or motivating factor depends on the type of institutional culture into which it is embedded. This means that the success of a proposed OER-related policy intervention is mediated by an institution’s existing policy <em>structure</em>, its prevailing social <em>culture </em>and academics’ own <em>agency</em> (the three components of what we’re calling “institutional culture”). Thus, understanding how structure, culture, and agency interact at an institution offers insights into how OER policy development could proceed there, if at all. Based on our research at three South African universities, each with their distinct institutional cultures, we explore which type of interventions might actually work best for motivating OER activity in these differing institutional contexts.</p>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it