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Record W2771366035

Connecting “The Roots of Society” with Conceptions of Citizenship through Time

2017· article· en· W2771366035 on OpenAlexaffabout
Gemma Porter

Bibliographic record

VenueThe Journal of Teaching and Learning · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCitizenshipTheme (computing)CurriculumEssentialismSociologyGlobal citizenshipRepresentation (politics)Frame (networking)Key (lock)EpistemologyPedagogySocial sciencePolitical scienceGender studiesLawPhilosophyComputer science
DOInot available

Abstract

fetched live from OpenAlex

This article examines the connections between the development of citizenship education in Saskatchewan and representations of the theme “roots of society” presented in the grade 9 Social Studies curriculum guides used in the province between 1971 and the present. The paper explores this connection by examining the development of the theme “roots of society” and the development of conceptions of citizenship. Conclusions concerning the characterization of citizenship in the curriculum guides were achieved through the implementation of key word frequency analysis. The key word frequency analysis served as the frame to identify and elucidate the representation of citizenship within the 9 Social Studies documents from 1971, 1991, 1999, and 2008. The examination of these curriculum documents revealed that developments in the conception and orientation of the “roots of society” are  reflective of changes and developments concerning notions of citizenship. The development of the “roots of society” and conceptions of citizenship education both follow a path from traditional/essentialist representations to critical social justice oriented models.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0050.049
Scholarly communication0.0070.007
Open science0.0010.007
Research integrity0.0010.002
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 head0.094
GPT teacher head0.386
Teacher spread0.292 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2017
Admission routes2
Has abstractyes

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