Coming into Life with Education: Definitions, Difficulty and Meaningfulness in Conceptual Aesthetics
Bibliographic record
Abstract
What do we mean by the word “education”? How do others know what we mean when the term is under constant revision? Do we even need definitive answers in order to speak meaningfully of it? This paper attempts to explore the potential for education’s meaningfulness via attention to its ordinary usages. In order to justify the need to be attentive to the specific instance of use, I will explore the closing down of conceptual meaning represented by acts of definition. In taking a closer look at what definitions of education try to do when they are articulated, I will follow a line of argument from Cora Diamond that the definition and explanation of a term can constitute a deflection from the difficult “reality” of educational discourse, a reality that poses its own problems in turn, but also should not be ignored. Attending to “education” as a word that appears with particular meanings in particular instances reveals the richness of the various forms it can assume. I describe this as a conceptual aesthetics 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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.140 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".