Bibliographic record
Abstract
In 2013, two authors of this book, Maggie Walter and Chris Andersen, published the original Indigenous Statistics: From Data Deficits to Data Sovereignty . These two scholars, one palawa, from Tasmania, Australia, the other Métis, from Saskatchewan and living in Alberta, Canada, met as board members of the then nascent Native American and Indigenous Studies Association (NAISA). Their shared interest in quantitative analysis led first to a recognition of their common experiences as Indigenous academics pursuing scholarship using primarily quantitative methodologies. Discussions around these similar experiences led to collaboration around their scholarship built around a shared understanding of the similarity of their experiences. The book they wrote from these was built around three central premises: Statistics are culturally embedded phenomena rather than neutral data All statistics are, in one way or another, culturally embedded rather than acontextual or neutral numbers. As such, Indigenous statistics reflect the purposes, assumptions and interests of those who have the power to commission, collect, analyse, interpret and disseminate the data, rather than necessarily reflecting the more robust complexity of Indigenous lived realities. For Indigenous Peoples in Anglo-colonized nations (Australia, Canada, Aotearoa New Zealand and the United States), the common trope of these data is one of deficit . The narratives that accompany these data have defined and continue to define, pejoratively, the relationship between Indigenous Peoples and their respective nation-states. The methodology, rather than the statistics themselves, are what create culturally “loaded” data Methods and methodologies are not interchangeable terms. Methods are the mechanisms through which data (in this case, statistics) are collected and analysed. Methodologies are the overall approach that shapes the research: what is considered worth doing; the underpinning assumptions; the key question/s asked; of whom; and why; and the framework through which the data are interpreted. Methodology, unlike method, therefore has almost nothing to do with data and everything to do with the socio-cultural positioning, value systems, knowledge systems and lifeworld of the researcher/data commissioning entity. Almost without exception (until recently, at least), for Indigenous statistics, that researcher/data commissioning entity has been non-Indigenous. Indigenous-led research shares similarity of methodology and legitimacy barriers This premise posits that all Indigenous researchers need to be more cognizant of the translative processes through which knowledge is translated into and out of the academy. This point was aimed, in part, at redressing the pointless, but often vigorously pursued, argument that quantitative research is culturally antithetical to Indigenous Peoples. Automatically positioning all quantitative research as positivist in approach, this claim asserts that such research is unable to reflect the culturally complex social relations—the lives and lived experiences, in other words—of Indigeneity. From our methodology-not-method premise, however, we know that it is methodological approach rather than the means of data collection that underpins the social meaning of research. Thus, Indigenous research that is framed by Indigenous perspectives and lifeworlds have more methodological similarities than differences, regardless of method.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.356 | 0.213 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".