Bibliographic record
Abstract
At the most fundamental level of analysis one may speak of educating as the act of someone saying and showing something to another in a way that leads the mind from where it is to where it might be, in the process widening horizons and fashioning habits of thought that make it possible for students to participate in the conversation that is their culture. The student stands to this conversation not only as learner but as initiate. Students appropriate habits, ideas, and questions that have their origin in the world of the ancients while the overriding imperative of the learning process is to take the conversation further in some respect and to ind their voice within it. In what sense, however, is conversation the heart and soul of education, and what is the nature and role of listening in education so conceived? At a time when qualitative matters place a distant second to quantiiables such as test scores, information retention, and marketable credentials, it falls to education theorists to remind us of what philosophers since ancient times have in one fashion or another maintained: that this practice has an identiiable orientation and purpose that transcends the order of the utilitarian.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.060 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".