Bibliographic record
Abstract
Education—so we might say—is different from untutored learning or unguided enculturation in that it directs the kind of learning that takes place. In this way education purposely shapes the subjectivity of those being educated. We could, therefore, say that the function of education is to ensure that people have certain kinds of experiences in order that they may achieve certain prespecified educational ends and so become certain kinds of people (such as people who can be creative, people who can solve problems, or work with scientific knowledge, or be politically responsible, and so on). Since, with education, it would seem that people are always socialized into a particular way of life, education can therefore be understood as planned enculturation. Educational environments are designed specifically to move a person— intellectually—from point A to point B. To do this we must know what the starting point is and what the finishing point is. For example to produce ‘creative’ people, or ‘politically responsible’ people we must first of all know the nature of the human subjects we are dealing with. Second, we must know what it means to be ‘creative’ or ‘politically responsible.’ Then we must have a plan or method to move A to B.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.043 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".