Bibliographic record
Abstract
Educational theorists have shown increasing concern over the need to ensure that citizens exercise values that consider the relevance of contributing even contradictory perspectives. Nussbaum (2004) has concentrated specifically on the contribution that literature provides in developing the moral imagination, a concept that is linked to the idea of cosmopolitan citizenship. This article reevaluates this particular position by examining the foundational role that value plays in Schwab’s (2004) vision of eclectic inquiry. An initial value attachment to a perspective or theory is seen by incorporating examples from outside the context of curriculum deliberation as a catalyst that stimulates effective eclectic inquiry in the face of criticism or contradiction. Following the recent work of Egan (1997), I argue that these value attachments can be initiated in an educational setting not simply by integrating more art classes, but by determining and isolating the essence of a value attachment to a novel and applying this interactive framework to all areas of study. Stimulating value attachments thus serves as a precursor to eclectic inquiry and contributes more significantly to the development of the moral imagination.
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 | 0.036 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.134 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".