Walking Many Paths, Our Research Journey to (Re)present Multiple Knowings
Bibliographic record
Abstract
Indigenous peoples globally are seeking new ways in which to communicate and share our worldviews. Sometimes defined as resistance research, emancipatory research, decolonising research - our research (re)presents the multiple journeys in which we live and come to know. Emerging Indigenous research methodological approaches are centring Indigenous ways of knowing, being and doing, to privilege Indigenous voices that have been suppressed through colonization. The intricate weaving of Western methodologies with Indigenous knowledges evokes agency in two emerging Indigenous researchers (from Australia and Canada) and weaves a path of reconciliation between their diverse disciplines as well as the seemingly dichotomous knowledge systems they are challenged to work within. Using metalogue, a way of authentically bringing together multiple voices through dialogue, we discuss the creative and radical Indigenous methodological approaches developed and enacted within our PhDs. The paper will provide insights to the epistemological, ontological and axiological principles that inform emerging Indigenous approaches to research.
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 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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.075 |
| Scholarly communication | 0.024 | 0.035 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".