An Intervention in Educational Inquiry: Re-membering, Honoring and Practicing a River’s Ways of Knowing and Being
Bibliographic record
Abstract
Answering this special issue’s call to reckon, repair and reworld, and following an ethical imperative to re-think social and educational structures, I turn to the wisdom of rivers. In the current settler colonial climate of near inertia that we live in, there is an urgent need to reckon with ways of being and knowing that go beyond the mainstream taken-for-granted habits of conventional educational research. Thinking with Indigenous perspectives, I problematize the Eurocentric worldview I was raised in and consider, in my capacity as a non-Indigenous educator and inquirer, some principles rivers can teach about educational inquiry. A series of photographs of the Chehalis River and personal vignettes allow me to trace and articulate a feminist and decolonial approach to my own inquiry. I consider how reciprocity, language and movement – three teachings gifted by the river – invite me to be, think, and act as an educator and an inquirer engaged in reconciliation. As many rich and diverse Indigenous perspectives have always reminded us, we have a responsibility to listen to and care for all our relatives, human and more-than-human. This is one important way we can work to transform our collective thinking, actions and future in 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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.041 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".