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Record W2928633655

Saskatchewan Secondary History Curriculum: Discourses of the Canadian Nation

2018· article· en· W2928633655 on OpenAlexaffabout
Gemma Porter

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCurriculumCitizenshipCritical discourse analysisNarrativeIdentity (music)PoliticsMythologyPolitical scienceSociologyGender studiesSocial sciencePedagogyHistoryLawIdeologyAestheticsLinguistics
DOInot available

Abstract

fetched live from OpenAlex

History education, with its emphasis on citizenship, identity, politics, and society, provides an interesting site of analysis to examine the dissemination of meta-narratives of the nation.  Part of a larger study focused on providing a historical critical discourse analysis of the development of the myths and meta-narratives of the nation as they appear within social studies, history and Native studies curriculum documents in the province of Saskatchewan from the 1970s through to the most recent curricular renewals, this study highlights conclusions regarding the Saskatchewan secondary history curriculum.   As a historical critical discourse analysis the study seeks to provide explanation concerning the function of the discourses of nation and also draw connections between the climate which gave rise to these particular discourses.  The study provides information useful in the examination of current conceptions and offers critical suggestions for future curricular development.  The study is limited to the official curriculum documents which contain the ‘aims and objectives’ for History 10, 20 and 30 in Saskatchewan.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.150
Threshold uncertainty score0.986

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0290.012
Scholarly communication0.0070.002
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.077
GPT teacher head0.336
Teacher spread0.259 · 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 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
Published2018
Admission routes2
Has abstractyes

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Same venue2019 Conference of the Canadian Society for the Study of EducationSame topicEducator Training and Historical PedagogyFrench-language works237,207