Concluding Thoughts on Methodological Resources and Research Challenges in Diverse Educational Sites
Bibliographic record
Abstract
This is the concluding chapter of Researching Practices Across and Within Diverse Educational Sites: Onto-epistemological Considerations. In this chapter, we recall the primary purpose of the book as to examine what it is that we believe we do as a diverse group of researchers from Australia, Finland, Norway, and Canada in educational research, reflexively considering our researching practices and how projects ‘turn out’ as a consequence of these practices. Having met and worked together as a part of the pedagogy, education, and praxis (PEP) international network, our collaborations offer us an important intercultural and cross-cultural opportunity to consider our positionality and responsibilities as researchers to our participants and co-inquirers, and their communities. Sharing our projects reveals the affordances and challenges offered by various methodologies unique from and common across our projects but also reinforces the imperatives of relationality, respect, and reciprocity between researchers and the communities they serve. We leave the readers with a concept that builds upon the title of this book, that of axio-onto-epistemology, already in use in postcolonial and decolonial/Indigenous scholarship, but raised here as an open invitation for all educational researchers to consider in their researching praxis.
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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 | 0.048 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".