Working with Indigenous Research Methodologies in the EER Context: Challenges and Opportunities
Bibliographic record
Abstract
Amid calls for Indigenization in engineering education and beyond, some Engineering Education Research (EER) scholars are focusing on Indigenizing engineering curricula and Indigenous inclusion in engineering spaces. Less discussed within EER contexts are Indigenous research methodologies, which are research methodologies predicated upon Indigenous paradigms that typically emphasize relationality. This paper aims to act as a resource for EER scholars interested in working with Indigenous research methodologies by providing an account of how one Indigenous research study was conducted. Through a critical reflective process, this paper addresses how methodological decisions were made in the context of a study and how those decisions were at once difficult and rewarding. The findings highlight several points in the qualitative research process where there were tensions between Western and Indigenous worldviews in the research process, including the role of the researcher, underlying research paradigms, and how those tensions were addressed.
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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 | 0.359 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.028 | 0.100 |
| Scholarly communication | 0.036 | 0.033 |
| Open science | 0.008 | 0.029 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".