Ulàpeitök: Using Bribri Indigenous Teachings to Develop a Ph.D. Research Methodology
Bibliographic record
Abstract
Although there is a growing interest in Indigenous research, education regarding how to put Indigenous research into practice is not often part of academic training. To increase the awareness of how Indigenous methodologies can be applied to academic research, we describe how we used Bribri Indigenous teachings to develop a Ph.D. research methodology for a food security project in Costa Rica. Our research approach was based on a Bribri concept related to cooperation, ulàpeitök; this concept guided our work and helped to reduce the negative consequences associated with conventional research with Indigenous people (e.g., extractive practices, reinforcement of gender inequality, misrepresenting cultural information). We identified three considerations that may be useful for other scholars applying Indigenous teachings to academic research: 1) build flexibility into the entire research program, 2) ensure that community-level and university-level researchers are willing to play multiple roles beyond those associated with conventional research, and 3) proceed with an ethic of friendship. Our work is relevant to scholars working in Indigenous/non-Indigenous research teams that aim to transform conventional research approaches to ensure that they support human rights, equity, and cultural continuity. In Costa Rica, our research is specifically relevant to building wider acceptance of Indigenous methodologies in higher education.
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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 | 0.032 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".