The idea of teaching about religion: an inquiry into the problem of meaning in education in a secular age
Bibliographic record
Abstract
What started out as a neat little argument for teaching about religion (AR) in public schools has become a wide-ranging essay asking why so many big ideas for education keep falling flat. The new argument, unifying the added themes, is that modern education is caught in self-defeating patterns of rationalizing and over-articulating its own meaningfulness and legitimacy. Thus, self-deception distorts the fulfillment of intergenerational responsibilities. The original topic has become a first example that leads into and illuminates the problem. As an educational idea, AR claims to address secularization for our times. If upon further thought the idea seems hollow, it becomes necessary to look again at the real world of secularization. AR reflects the contemporary obsession with diversity and the compulsion to turn education into a parade of possibilities. What is taught is merely a rationalized stance. Indeed, given that the legitimacy of an education system depends on locating authority within a recognizable source of meaning, and given that modernity foregrounds incommensurable diversity, there is an apparent obviousness to grounding the educational enterprise in bare proceduralism and then topping it up by tenuously claiming association with various deeper sources. But George Grant’s characterization of the religious education of an earlier generation still holds: “a few thin platitudes.” In religious contexts, a distinction is sometimes made between religious instruction and formation within thick tradition and community. Even in a secular age, the young deserve some kind of thick formation. Yet that seems unimaginable, because contemporary common sense is caught in what Hubert Dreyfus calls theoretical holism. Secularization presents education not with an array but with a dilemma: To go on trying to manage meanings for the young, or to allow them to find meaning in strong practices? Facing this dilemma will entail facing the disenchantment generated in our deepest Western educative impulses. Rediscovering true sources of educational authority for our times will entail going back to the origins of modern schooling in the breakup of the apprenticeship model and rethinking an institutional solution that so fundamentally denies the way in which human beings become oriented to meaning through strong practices.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.110 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".