Critical pedagogy and digital liberation: A Freirean debate for SoTL
Bibliographic record
Abstract
This article examines digital inequality through the lens of critical pedagogy, as defined by Paulo Freire, and as articulated by bell hooks. It posits that digital inequality is an extension of broader socio-cultural disparities and scrutinises the implications for lecturers and students in a digital-centric academic environment. The discussion extends beyond the mere availability of digital tools and addresses the need for institutional paradigm shifts fostering creative practice that empowers all participants in the educational process to engage with digital tools effectively. The critique of ‘Technology Enhanced Learning’ (TEL) focuses on its role in Higher Education and employability, advocating for a reevaluation that centres on the individuals most impacted by digital educational policies.
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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 | 0.030 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.125 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".