The Role of the Disciplines in Cardinal Newman's Theory of a Liberal Education
Bibliographic record
Abstract
For over a century Cardinal Newman's contribution to the idea of a liberal education has received widespread attention. His basic position, that a liberal education conslsts in a well-informed judgment combined with an ability to perform the different logical operations involved in reasoning well, has frequently been criticized on the grounds that it failed to cater to religious and moral training. However, this is not an altogether legitimate criticism. Newman never intended his liberal education to be the only education the university student would receive. It was to form just a part, albeit an extremely important part, of a university undergraduate education-1 Nevertheless, while a substantial portion of the literature on Newman's educational thought is devoted to discussion of this issue, when in fact no real problem exists here, other and more genuine difficulties have been overlooked. Accordingly, it is not my intention to further the already protracted debate concerning the appropriateness of a liberal education as understood by Newman at the university level. It is on one of the real problem areas, specifically on Newman's idea of the possibility of scientific knowledge and its implications for the curriculum of a liberal education, that I wish to focus attention.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.052 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| 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".