Bibliographic record
Abstract
Our paper aims to justify and illustrate the moral and epistemic value of the inter-philosophy dialogue (IPD) methodology we use in our Indigenous-non-Indigenous research collaboration. Our goal is to show researchers, particularly global health researchers (GHRers), that IPD supports i) moral and epistemic respect in collaborative relationships, ii) clarity about normative assumptions and presuppositions, iii) sound argument-chains supporting empirical and normative conclusions, and iv) new normative paradigms for research. In Part I, we show why a philosophical methodology developed in the context of Aboriginal rights research has applicability in the context of global health. In Part II, we demonstrate the use and success of IPD in our collaborative research and co-authoring. In Part III, we justify our confidence that GHRers using IPD can produce analogous results.
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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 | 0.110 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.103 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.009 | 0.013 |
| 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".