Framing Open Educational Practices from a Social Justice Perspective
Bibliographic record
Abstract
OEP (open educational practices), inclusive of open pedagogy, is often understood with respect to the use of OER (open educational resources) but can be conceived with more expansive conceptualisations (see Cronin & McLaren 2018; DeRosa & Jhangiani 2017; Koseoglu & Bozkurt 2018). This article attempts to build on existing OEP research and practice in two ways. First, we provide a typology of OEP, giving examples of practices across a continuum of openness and along three axes: from content-centric to process-centric, teacher-centric to learner-centric, and practices that are primarily for pedagogical purposes to primarily for social justice (Bali 2017). Second, we employ Hodgkinson-Williams and Trotter’s (2018) conceptual framework, which builds on Fraser’s model of social justice, to critically analyse the ways in which the use/impact of OEP might be considered socially just, with a particular focus on expansive, process-centric OEP. We analyze for whom and in which contexts OEP can (i) support social justice along economic, cultural and political dimensions, and (ii) do so in transformative, ameliorative, neutral or even negative ways. We use the typology and framework to analyse specific process-centric forms of OEP including collaborative annotation, Wikipedia editing, open networked courses, Virtually Connecting, public scholarship, and learner-created OER. Analysing specific practices highlights diversity across the axes and subtle differences among them, such as when a particular practice is considered good pedagogy and how it can be modified to be more oriented towards social justice. We discuss limitations of each practice not just from its discourse and design, but also how it works in practice.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.017 | 0.125 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".