Convergence of Indigenous Science and Western Science Impacts Students’ Interest in STEM and Identity as a Scientist
Bibliographic record
Abstract
Within the context of North American Indigenous culture, certain Elders are respected gatekeepers to Indigenous science, also known as traditional knowledge. Yet, while North American born minorities such as Black Americans, Amerindians, and Latin Americans may hail from cultures with a similar appreciation of their own Indigenous science Elders, these minority groups are especially underrepresented in Western science, technology, engineering, and math (STEM)-both in academia and in the workforce. North American underrepresented minorities experience high attrition rates in academia generally, and in STEM specifically. Canada's Truth and Reconciliation Commission makes a call to action to Indigenize education to benefit all students. Herein lies an opportunity to investigate the impact of Indigenization of a Western science biochemistry course to assess the impact upon university students, both minority (non-White) and non-minority (White) in Anglophone North America (Canada and USA). The aim of the study is to investigate the impact of an Indigenized Western science online course upon student interest in STEM, student perception of the relevance of Elder co-instructors, and student identity as a scientist. A pedagogical quasiexperiment was conducted at North American tribal colleges and mainstream research-intensive universities, regarding an online science course taught either with or without Elder co-educators alongside PhD STEM-trained instructors. Student perceptions of the value of Elder co-educators did not differ across groups and remained unchanged after course delivery. Findings also show that after taking the course co-taught by Indigenous science Elder co-educators, students have significantly greater interest in STEM than those students not exposed to Elders' teachings. Non-White students reported significantly less self-identification as a scientist than did White students at pre-course, but reported similar identity as a scientist to White students post-course. We attribute these findings to the impact of culturally competent course content to minority students especially. This work establishes the relevance of using online technology to Indigenize a Western science course taught internationally, and suggests the need for more investigative work toward the convergence of Indigenous science and Western science in academia.
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 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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".