Mele as methodology: crafting (k)new tools for Indigenous research
Bibliographic record
Abstract
Purpose In Decolonizing Methodologies (1999), Linda Tuhiwai Smith asserted that “the master’s tools of colonization will not work to decolonize what the master built.” Smith challenged Indigenous researchers to fashion “new tools for the purpose of decolonizing and Indigenous tools that can revitalize Indigenous knowledge” (p. 22). A quarter of a century later, this paper reflects on the powerful impact that Smith’s call to action has had upon recent generations of bright, politically active and culturally grounded Native Hawaiian researchers, many of whom are innovatively turning to the Native epistemologies embedded in our traditional cultural practices to craft (k)new research tools and methodologies. Design/methodology/approach This paper features three Native Hawaiian scholars who are simultaneously hula and mele (traditional Hawaiian dance and song) practitioners and who instinctively turned to their hula training to guide and indigenize their research practice. Findings Each of these three scholars describes how they creatively applied the Hawaiian epistemologies embedded in their hula and mele training to fashion (k)new, Indigenous methodologies to guide (1) their research conduct, (2) their data analyses or interpretations and (3) the presentation of their research findings, respectively. Originality/value These three Hawaiian scholars and hula practitioners represent a larger groundswell of Native Hawaiian researchers who are bravely and creatively drawing upon the traditional wisdom and sensitivities embedded in our cultural practices to craft and wield (k)new research tools to “dismantle the master’s house” (Lorde, 1981) and build an Indigenous hale (house) of our own.
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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 | 0.240 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.010 | 0.115 |
| Scholarly communication | 0.024 | 0.032 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".