A posthumanist critique of flexible online learning and its “anytime anyplace” claims
Bibliographic record
Abstract
Abstract Flexible approaches to online learning are gaining renewed interest in some part due to their capacity to address emergent opportunities and concerns facing higher education. Importantly, flexible approaches to online learning are purported to be democratizing and liberatory, broadening access to higher education and enabling learners to participate in educational endeavours at “anytime” from “anyplace.” In this paper, we critique such narratives by showing that flexibility is neither universal nor neutral. Using critical theory, we demonstrate how flexibility assumes imagined autonomous learners that are self‐reliant and individualistic. Through relevant examples, we show how such a framing to flexibility is oppressive, and argue that a contextual, relative and relational understanding of flexibility may in fact be more liberatory. Such an approach to flexibility, for example, may involve contextual and relational efforts to relax prescribed curricula within courses or programmes of study.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.110 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| 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".