A Pragmatic Utopia? Utopianisms and Anti-utopianisms in the Critique of Educational Discourse
Bibliographic record
Abstract
This paper seeks to address what I claim are competing utopian and anti-utopian impulses within educational discourse aimed at formulating a just and fair conception of public education. On the one hand, there is a tendency to prescribe concrete utopias – normative blueprints that claim to portent how a redeemed public education will (and ought to) be. On the other hand, there is the tendency to prescribe material revolutions – strategic blueprints that dictate the kinds of political action that educators must undertake in order to bring about lasting social change. I argue that both of these approaches to formulating a just conception of public education are flawed for pragmatic as well as normative reasons. As a way of avoiding the pitfalls inherent to utopianism and anti-utopianism, I suggest that those of us interested in a just conception of education maintain our focus on a kind of pragmatic utopianism. While pragmatic utopianism requires that we abandon the notion that we can ever know what a redeemed public education will look like in its particulars, it does set out standards of deliberation that can increase the likelihood that we will be able to address issues of educational justice as they arise.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.092 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".