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Record W6983186587

Little Mosque on the Prairie - “The Barrier” (season 1, episode 2)

2013· article· en· W6983186587 on OpenAlexaboutno aff

Bibliographic record

VenueUST Research Online (University of St. Thomas - Minnesota) · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicMedia, Religion, Digital Communication
Canadian institutionsnot available
Fundersnot available
KeywordsTSG101SubpoenaNucleofectionPretextHyporeflexiaTubulopathy
DOInot available

Abstract

fetched live from OpenAlex

Interfaith discussion facilitated by Odeh Muhawesh, Noreen Nazir, and Rabbi David Wirtschafter\nThis is the third in a series of three discussion sessions featuring selected episodes of Canada’s popular television sitcom Little Mosque on the Prairie. This fun, light-hearted, and comedic show raises several practical everyday issues facing North American Muslims and people of other religions. Can a mosque be in a church? How diverse is Islam? What role does Western culture play in the formation of an Islamic community? These questions and others will be discussed at these gatherings.\nFacilitators: Odeh Muhawesh, a special consultant for the Jay Phillips Center, earned his doctorate at Qum Seminary-Global Institute for Islamic Studies in Iran where he focused on comparative theology and Islamic jurisprudence. As an adjunct professor at the University of St. Thomas, he teaches courses in Islamic theology and the modern history of the Middle East. Noreen Nazir, a Catholic from Pakistan, holds an MA degree in Islamic Studies from Luther Seminary and has worked with the Christian Study Centre in Rawalpindi and the National Commission for Interfaith Dialogue and Ecumenism in Pakistan. Rabbi David Wirtschafter regularly serves as rabbi-in-residence with the Jay Phillips Center and is the rabbi of the Ames Jewish Congregation in Iowa.\nSponsored by the Jay Phillips Center in collaboration with St. Pascal Baylon Catholic Church

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.095
GPT teacher head0.289
Teacher spread0.195 · 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.

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
Published2013
Admission routes1
Has abstractyes

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