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Record W4254463279 · doi:10.5040/9781474248228

Civics and Citizenship Education in Australia

2016· book· en· W4254463279 on OpenAlexaboutno aff

Bibliographic record

VenueBloomsbury Publishing Plc eBooks · 2016
Typebook
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsCivicsAustralian CurriculumCitizenshipCitizenship educationCurriculumNational curriculumSubject (documents)Context (archaeology)IndigenousGlobal citizenshipSocial studiesPolitical scienceSociologyPedagogyGlobal citizenship educationSocial scienceProject commissioningGeographyLawPoliticsPublishingLibrary science

Abstract

fetched live from OpenAlex

Civics and Citizenship Education in Australia provides a comprehensive analysis of teaching and learning in this field in Australian schools, drawing on case study material to demonstrate the current practice in the field. Reflecting on the issues and possibilities raised by the inclusion of civics and citizenship education in the new national Australian curriculum, leading national and international scholars analyse the subject’s theoretical, curricular and pedagogical bases and approaches. Placing civics and citizenship education within historical and contemporary contexts, the book critically explores a range of issues concerning the development, organisation and teaching of the subject. These include how the subject might include indigenous, global and Asian perspectives, and how it may help students to engage with issues around sustainability, active citizenship, diversity, religion and values. The final chapters written by scholars from England, the USA, Canada, Hong Kong and Singapore adopt a comparative approach situating Australian civics and citizenship education in the wider international context.

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)0.001
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.047
GPT teacher head0.334
Teacher spread0.287 · 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
GenreOther

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

Citations10
Published2016
Admission routes1
Has abstractyes

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