A ‘Race Course,’ ‘Running,’ and a ‘Chariot’: Using the Katha Upanishad to Inform a Curriculum of Selflessness
Bibliographic record
Abstract
Jackson (1992) demonstrates that most dictionaries define curriculum as a “course of study.” However, he also includes the interpretations; “a race course,” “running,” and “the chariot used in races.” Using a critical interpretive practice of genealogy to organize discourses, Roy first characterizes learning as a yearly “race” around a track where learners pick up a “course of study.” Then, using Pinar’s (1975) method of currere to correspond with “running,” the author shifts from the “race course” to the “runner” (or the learner) and address the learner’s past, desired future, and present context. Finally, drawing upon a “chariot” analogy taken from The Upanishads of Indian Vedantic literature, she probes further into the realm of cognition and provides a philosophy, based on the yoga of selfless action, to inform the runner and the course of study. The paper ends with a bid for a curriculum of selflessness.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.006 |
| 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".