“History Repeats itself, First as Tragedy, Second as Farce”: Reclaiming the Enchantment of Teacher Education
Bibliographic record
Abstract
“ Those who cannot remember the past are condemned to repeat it.” History repeats. But is that always what has gone before? “History never repeats itself in the same way.” In teacher education, that gives us hope to re-invent the process in imaginative ways to avoid that: “History repeats itself, first as tragedy, second as farce.” The re-imagining of teacher education must avoid the farcical repetition of a historical liberal turn as opposition to the neo-liberalist measurement of human capital. Teacher education is now a policy problem. Administrative regimes hold teachers responsible for student learning and teacher educators accountable for preparing teachers as well-adjusted policy robots. Teacher education has lost its way. We need a re-enchantment, one that includes an appreciation of mystery. Mystery speaks to the soul, to the depths of the pedagogical heart and imagination, where we find value, meaning, and oneness with the world enabling a fulfillment that makes life purposeful and full of passion. We need teachers who appreciate wonder and story, together with a respect for other cultures and life’s simplicities, rather than being neurotically driven to hyperactivity by an emphasis on performativity.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.070 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| 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".