Oh Canada: Citizenship on First Nations’ Land
Bibliographic record
Abstract
There is extensive scholarly research on the teaching and integration of citizenship education within formal learning environments (Knight Abowitz & Harnish, 2006; McLaughlin, 2006). Within Ontario it is an integral component of the social studies program developed by the province (Ministry of Education, 2004b), with a distinct focus on what it means to be Canadian (Brodie, 2002) within the grade 5 curriculum (Ministry of Education, 2004b). While these topics have been studied there has been limited work completed on the teaching of citizenship, especially Canadian citizenship, to First Nations students. With some researchers stating that First Nations should not be in a state of subjection to the Canadian government (Battiste & Semaganis, 2002), there are First Nation members who argue that they are First Nation and do not want to be recognized as Canadian. Therefore, the goal of this phenomenological study was twofold. The first goal was to examine how a teacher, working within a school in a First Nation community planned, delivered and assessed the Ontario grade 5 social studies curriculum titled “Aspects of Citizenship and Government in Canada” (Ministry of Education, 2004b, pg. 8). The second objective was to understand how the students and community members, including the teacher and Elders understood the concept of citizenship. After I was able to respectfully obtain assent for completing the research within a community in southwestern Ontario, data collection took place through interviews of grade five students, the teacher and the elder who was a guest speaker in the class. Field notes were also collected by watching the delivery of the unit in the class by the teacher. The results of this study demonstrated the complexity of delivering this unit in a school located within a First Nation community, as the teacher had to balance the expectations of the curriculum along with the expectations of the community. The study also provided an opportunity to start understanding the multiple ways citizenship is understood by different members of the community.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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.
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".