(Let Him Speak Strong): Integrating First Peoples Principles of Learning for Students' Success
Bibliographic record
Abstract
This project, completed as part of a Master’s in Education with a focus on Curriculum and Instruction, has two major purposes: To investigate the needs of Aboriginal learners; and to make an easily accessible guide to encourage educators to incorporate the First Peoples Principles of Learning into their practice. It begins with a brief overview of the history of the Aboriginal peoples of Canada and their relationship with the education system. Next, a thorough review of the literature is offered in an effort to better understand what Aboriginal students need in order to be successful, as well as what factors often influence teachers’ ability and/or willingness to begin to include both Aboriginal content and ways of knowing into their practice. This review of the literature resulted in the creation of a website that is designed to act as a handbook of sorts, offering information about Aboriginal Education and ways to apply the First Peoples Principle of Learning in a meaningful way, using service learning as a tool. The entire project has strived to reflect the First Peoples Principles of Learning, in all steps of its completion. In conclusion, this project aims to guide educators in their efforts to increase levels of success amongst Aboriginal learners and to encourage educators to begin to think about the importance of both Aboriginal content and culture in our classrooms. The result is an informative and practical addition to the conversation about Aboriginal education.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".