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From “Text” to Context in the Teaching of World Religions

2009· article· en· W819419589 on OpenAlexaboutno aff
W. Marston Acres

Bibliographic record

VenueThe Journal of the World Universities Forum · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicReligious Education and Schools
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)SociologyHistoryArchaeology

Abstract

fetched live from OpenAlex

While most “World Religions” curricula are employed in a wider view to liberal arts education, rather as specifically theological or religious-centered, there are several approaches which can be made to form a more constructive learning working with the existing “textbooks” circulating (treated here in English) in a kind of metaphysical way in diverse cultures whose needs are more concrete. This paper addresses two key ways in which the popular “World religions” curriculum can be, to paraphrase Tomoko Masuzawa, “re-invented” as an integral aspect of learner-based pedagogy. First, by taking the disembodied “text” and analyzing its role, content, and purpose as a learning object by situating it in the world of internet, online education, the context of learning will be seen to require what might be called energetic “points of reception”. Those points of reception can, in this second case, be seen as an individual and contextual reworking of the “world religions”, not by denying the content of the textbooks, but by subjecting its view to the actual societies, schools, and nations which employ them. While much scholarship has recently examined “religion”, “world religions”, and their definitions, this paper works on two broad themes (reception and contextualizing knowledge) to imagine avenues of engaging with what Canadian philosopher Charles Taylor calls “social imaginaries” in which students discern their own sociological, philosophical, and political imaginations on global and local religious cultures.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.299
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

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