Bibliographic record
Abstract
International Journal of Multiple Research Approaches, 8(1 ), 117-128.We raise a question for contemplation regarding the article by Amrita Roy that seeks to reframe epidemiological research methodology to overcome incongruence with Aboriginal worldviews (Roy, 2014). We ask if this article is a 'Trojan Horse' - an alluring gift on the outside but lurking with danger within? Many readers will open their minds to the gift of the comprehensive critique of the danger of positivist methodologies in Indigenous research settings. The arguments are perfectly sound from a western perspective. However, the danger lurking within is the cryptopositivist approach of this article as a whole (Friedrich, 1992). The article repeatedly maps western methodological concepts to conceptions of Indigenous worldviews such as relationality.This is an inherently positivist approach. It collapses the experience or the being of relationality, which as an entire ontology is beyond concepts, down to the level of concept. In attempting to reframe western concepts it must frame Indigenous ways of being - it is impossible to map one to another until they are reduced to the same level -the nonconceptual must become conceptual. This act of reduction is a subtle but powerful act of colonisation. We need to consider to what extent the very act of research is an act of reduction or 'fall' to the conceptual, abstracting any ' thing' from the whole disembodies what is ultimately relationally embodied.I can remember what my father (SW) has told me about his earliest memories. He grew up living in a tent on the trap-line, out in ' the bush' of northern Manitoba:I can remember being alone in the forest, but I didn't ever feel that I was actually alone. There were the trees there with me, the smaller plants and animals around in the forest and the river was nearby. The whole forest was there keeping me company.As researchers, we can enumerate all the trees that grow in the area where my father grew up. We can name the different species of trees, their average age depending upon when the last forest fires cleared the area, the mean and range of sizes of the trees, and discover the common diseases and parasites that infect trees in the area. All of these measurements can be done via research demonstrating great rigour and validity. However, others would also be right in pointing out that we may have lost sight of the forest through enumerating the trees.Yes, epidemiological methods can be very useful tools. …
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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 | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.044 | 0.025 |
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".