Advancing Equity and Empowering Science Students from Indigenous Communities
Bibliographic record
Abstract
Implementation of Diversity, Equity, Inclusion, and Respect (DEIR) is crucial for supporting students in a culturally safe environment, reducing bias, fostering respect, broadening perspectives, enhancing collaboration, and improving education in science. DEIR with Indigenous reconciliation incorporates Indigenous-based DEIR initiatives as a response to the Truth and Reconciliation Commission (TRC) in Canada to acknowledge the intergenerational trauma and mistrust toward colonial institutions such as universities. Universities can advance reconciliation by incorporating DEIR with Indigenous reconciliation into everyday practices. Indigenous students are significantly less likely to attain degrees in science, technology, engineering, and mathematics (STEM). The lack of Indigenous representation in STEM significantly hinders the inclusion of Indigenous perspectives in scientific processes and decision-making. A collaborative effort is essential to improve Indigenous student recruitment in science programs. Scientists need to educate themselves on the colonial legacies of Indian Residential Schools (IRSs) and Indian Hospitals (IHs), as well as on the biases and barriers that Indigenous students face. While this paper focuses on several Canadian examples, it highlights challenges and traumas that are similarly faced by Indigenous students worldwide. Therefore, supervisors and research groups should actively participate in training sessions and develop strategies aimed at preventing discrimination and fostering inclusivity. This paper highlights the importance of DEIR with Indigenous reconciliation in university science programs and addresses issues of recruiting Indigenous students. The "Calls to Action" outlined in this paper will help scientists and educators understand barriers faced by Indigenous students, advocate for lifelong learning and social stewardship, and foster a more inclusive scientific environment.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| 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; a candidate call from one teacher head, 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".