Re-Working Statistics: An Indigenous Quantitative Methodological Approach to Labour Market Research
Bibliographic record
Abstract
Indigenous labour market statistics are a key technology through which the Canadian nation-state reaffirms its possession of Indigenous land. Colonizing settler norms, values, and racialized understandings inform the dominant methodological approach to Indigenous labour market statistics resulting in the persistent production of deficit-based, racialized statistical depictions of Indigeneity. The purported objectivity and neutrality of quantitative data, however, obscures the racialized origins and parameters of dominant statistical research on Indigenous labour market outcomes. This thesis denaturalizes the dominant methodological approach to Indigenous labour market statistics. The process of denaturalizing the dominant quantitative methodology undertaken in this thesis is twofold. First, I explicate colonizing power relations at three different levels of abstraction to expose the dominant social, cultural, and racial terrain from which Indigenous labour market statistics emerge. I engage with Marxist theories of capitalism and Aileen Moreton-Robinson’s (2015) theorization of patriarchal white sovereignty to construct a general framework for theorizing colonizing settler societies, before drawing on Indigenous labour histories and critical Indigenous demography to refine this framework to the particular Canadian context. Using this framework, I conduct a critical analysis of quantitative academic research on Indigenous labour market outcomes. Second, I explore the development of an Indigenous quantitative methodology in the context of work and labour research. I discuss three strategies for advancing an Indigenous quantitative research agenda on work and labour, before translating one of these strategies into practice. Specifically, using data from the General Social Survey 2016, I explore the development of a statistical model that focuses on structural inequality rather than Indigenous deficit.
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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 | 0.114 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".