Institutional Culture and OER Policy: How Structure, Culture, and Agency Mediate OER Policy Potential in South African Universities
Bibliographic record
Abstract
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). 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 structure, its prevailing social culture and academics’ own agency (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.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".