Midwifing liberatory education futures with education fiction: on the need to cultivate decolonial imagination
Bibliographic record
Abstract
This paper argues that for education fiction to be a valuable methodology for studying education technology and futures, it must be rooted in the context of our current era, marked by polycrisis, collapse, and the enduring influence of colonial modernity. We begin with a reflexive analysis of academic knowledge production and then explore how education fiction can inadvertently reproduce colonial logics of these contexts are ignored. Drawing on the Gesturing Towards Decolonial Futures Collective’s concept of ‘hospicing modernity,’ we propose cultivating a decolonial imagination, one that holds grief, complicity, and possibility, as a strategy for midwifing liberatory education futures. We further argue that education fiction can function as a prefigurative method: a space to practice relational, decolonial ways of knowing and worldmaking. This approach is especially urgent in relation to education technologies, which remain deeply entangled with extractive systems.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.078 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| 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".