Bibliographic record
Abstract
While acknowledging that relations do matter in education, in No Education Without Hesitation, Gert Biesta cautions us against temptation to focus too much on relational — for example, when we seek to know as much as we can our students, about their history, their background, their identity, their feelings, their sense of self. The risk, he writes, is that: (B)y focusing too much on relational dimensions of education, we lose sight of gaps, fissures, and disjunctions, disconnections, and strangeness that are part of educational processes and practices as well; and, more importantly, we run risk of losing sight of educational significance of these dimensions. Central to Biesta's essay, and drawing on work of Jacques Ranciere, is an assumption that child is already a speaking subject. If education starts from there, Biesta says — rather than from an assumption that child is one who cannot yet speak but is moving toward that capacity or ability — an entirely different educational project opens up. He goes on to explain that claim, the child is speaking, is a political and educational claim, not an empirical one, and he describes three different ways in which we might enact it: listening, recognition, and experience of being addressed.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.096 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.013 |
| 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".