Upholding “the educational” in education: Schooling beyond learning and the market
Bibliographic record
Abstract
Abstract This article argues that schooling’s driving purpose should be to educate. Given heightening global crises and the potential of education to respond, we agree with the spirit and focus of UNESCO’s (2021) A new social contract for education intervention. Education/schooling should be motivated by progressive, critical visions to contribute to more sustainable and just human and planetary futures. Embodied-affective-cognitive technologically-mediated processes of becoming educated, however, are not a force that can smash injustice or ecologically destructive capitalism. Educationally speaking, there is no shortcut to cultivating students as “change agents” for sustainable futures. Hannah Arendt’s essay “The crisis in education” (2006) is instructive in clarifying the function of schooling and in categorically distinguishing adults from children, education from politics, and education from learning. While human learning proliferates in multiple ways independent of existential/ethical mooring, education ultimately requires committed adults spending time with, and socioemotionally and intellectually supporting, children to deepen their understanding of the world and others, giving meaning and significance to their/our lives as part of larger collectives called upon to sustain and renew a common world.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".