The Cruel Optimism of Education and Education's Implication with ‘Passing-on’
Bibliographic record
Abstract
In this article I draw on Lauren Berlant's notion of ‘cruel optimism’ to identify and untangle how the prevailing sense of ‘optimism’ in education works against our common hope or collective striving for what is educational in education. In particular, I discuss how the ‘cruel optimism’ that invites individuals to constantly innovate and improve themselves through ever more learning leads ultimately to a sense of ‘presentism’, ‘privation’ and ‘loneliness’, which comes to threaten the role that education plays (or should play) in sustaining and forging a common world. Proposing that education is where the concern with ‘passing-on’ (in all senses of the word) properly takes place, I discuss how education can tend to and pine towards something larger and more durable (the world) than the individual acquisition of knowledge and skills that serve immediate transient interests. As an exemplar of a place of ‘passing-on’, I ask us to consider how education invites us to affirm the ‘living-on’ of the question of what it might mean to live together after all: to forge, sustain and pledge something of significance in common (and across generations) amidst what is constantly passing away. In this sense, I seek to gesture to the possibility of hope (as opposed to a mere optimism) within education: a sensibility and affirmation for ‘passing-on’ and ‘sur-vivance’. Such a hope might help to address the cruel depravity and isolation affecting our time that is caught up in the ‘learnification of education’.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.084 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.012 |
| 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".