Aboriginal Ways of Knowing and Learning, 21st Century Learners, and STEM Success
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aboriginal people are alarmingly under-represented in science, technology, engineering, and mathematics (STEM)-related careers. This under-representation is a direct result of the lack of academic success in science and mathematics, an issue that begins early in elementary and middle school and often escalates in secondary school with the majority consequently doing poorly, not completing these courses and often dropping out. This makes them ineligible to pursue STEM-related paths at the post-secondary level. The greatest challenges to success in these courses are the lack of relevancy for Aboriginal learners and, as importantly, how they are taught; impediments that are also paramount to the increasing lack of success for many non-Aboriginal students in STEM-related courses. This paper explores how Aboriginal ways of knowing and learning and those of the 21st century learners of today very closely parallel each other and illustrates how the creative multidisciplinary approach of a liberal education might be the way to enable early academic engagement, success and retention of Aboriginal learners in the sciences and mathematics.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it