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Record W2998813900 · doi:10.31756/jrsmte.233

Science Education under a Totalitarian Theocracy:  Analyzing the ISIS Primary Curriculum

2019· article· en· W2998813900 on OpenAlexaff
Patrice Potvin, Marianne Bissonnette, Chirine Chamsine, Marie-Hélène Bruyère, Mohamed Amine Mahhou, Olivier Arvisais, Patrick Charland, Stéphane Cyr, Vivek Venkatesh

Bibliographic record

VenueJournal of Research in Science Mathematics and Technology Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsConcordia UniversityUniversité du Québec à Montréal
Fundersnot available
KeywordsTheocracyIdeologySecularizationCurriculumIslamContent analysisContent (measure theory)PoliticsScientific literacyPolitical scienceSociologyPedagogySocial scienceScience educationLawPhilosophyTheology

Abstract

fetched live from OpenAlex

We conducted an unprecedented analysis of the Islamic State of Iraq and Syria (ISIS) primary school science curriculum. The research question focuses on the general scientific quality of the five documents examined, the integration of religious content and the possible tensions between science and religion that result from including such material in the corpus. This content analysis also focuses on the ideological/political agenda that supports its content and structure. Conclusions argue that the ISIS science curriculum appears to be committed to an absolutist/theocratic ideological program that, among other things, promotes a very inadequate concept of scientific activity and content. Recommendations about secularization and the reconstruction of post -ISIS education systems are formulated.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0020.003
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.050
GPT teacher head0.454
Teacher spread0.405 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
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

Citations4
Published2019
Admission routes1
Has abstractyes

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