Bibliographic record
Abstract
This article discusses the teaching model of civics education and the structure and content of the civics education curriculum in Canada. Aims to provide an understanding of the teaching model of civic education in Canada as well as the structure and learning model in Canada both in school and out of school for the younger generation as well as the teaching the concept of citizenship for immigrants is done through a comprehensive and inclusive approach. a comprehensive and inclusive approach. For local residents, civic education civic education is designed to form citizens who are aware of their rights, responsibilities as citizens, appreciate diversity and participate in society. in society. While civic education instruction for immigrants is supported by governmentcontent of the civic education curriculum in Canada. The research method used is a literature study with a qualitative approach that describes descriptively, secondary data sources in the form of relevant literature in the form of books and various journal articles used to support research. Results research showed that the teaching of civic education learning model civic education -funded organizations, service providers, community centres, and housing agencies, it aims to help immigrants understand their rights, culture, responsibilities, and responsibilities in Canada. housing agencies aims to help immigrants understand their rights, culture, and responsibilities in Canada. The content of the civics education curriculum in Canada is governed by section 93 of the Canadian Constitution Act 1867, exclusive legislative responsibility for education is given to the provinces and territories. Although there are differences in teaching and curriculum content, they share the goal of providing students with the opportunity to learn about citizenship from a variety of perspectives. from a variety of perspectives.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".