Getting Them Through the College Pipeline: Critical Elements of Instruction Influencing School Success Among Native Canadian High School Students
Bibliographic record
Abstract
As a consequence of the Civil Rights Movement and related social movements, the past 30 years have witnessed an unprecedented rise in higher education enrollments among ethnic minority groups, women, and low-income students, as well as increases in the financial aid available to these groups. However, certain ethnic minority populations, such as Native American and Native Canadian students, still experience difficulty in the transition from the K-12 school system to higher education, despite policies enacted to increase access. The research literature cites the disjuncture between the home cultures of these students and the environments of the school as a major cause of the failure of Native students to make the transition from the K-12 school system to higher education institutions. These findings have prompted calls for the integration of Native cultural knowledge and perspectives into the school curriculum. This Canadian study examined the outcomes of consistently integrating Native perspectives into the high school social studies curriculum throughout the 2003–2004 academic year. The teachers integrated Native cultural learning objectives, resources, and instructional methods. Critical elements of the integration processes that appeared to increase academic achievement, class attendance, and participation among Native students 1 are discussed. Teachers can draw on these elements to implement effective teaching strategies for the preparation of Native students along the college pipeline.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".