Bibliographic record
Abstract
The education of Aboriginal youth is, in some respects, in crisis. Aboriginal communities in Ontario are as a group currently experiencing marginalization within the education system. As such it is imperative that efforts be made to better understand the system to improve the success rate for Aboriginal youth. The Ontario First Nation, Métis and Inuit Education Policy Framework (2007) has committed to “improve achievement among First Nation, Métis and Inuit students and to close the gap between Aboriginal and non-Aboriginal students†. English and Language Arts teachers are compelled to consider how the policy discourse of the 2007 Aboriginal Policy Framework implicates upon the socio-political and socio-historical currency of literacy in their instruction. Consequently, this qualitative study examined one component of a large-scale project, in the tradition of grounded theory, including the implications of Aboriginal education policy discourse on literacy instruction as it applies to over 200 prospective teachers enrolled in a Teacher Education Program in Ontario, Canada. Participants identified two themes that they believed Aboriginal students would find most challenging, including: tension with provincial curriculum and, feelings of misrepresentation.
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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.050 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".