Who is Aboriginal? Variability in Aboriginal Identification between the Census and the APS in 2006 and 2012
Bibliographic record
Abstract
Over the last 50 years, censuses have shown very substantial increases in the estimated sizes of Aboriginal populations in settler states such as Canada. Since these increases cannot be explained by demographic factors alone, authors have proposed that, as the situation of Aboriginal people has been under increasing public scrutiny, it has become more socially acceptable to report that one is Aboriginal. This may be an explanation for increases between censuses that are conducted five or ten years apart, but is such an explanation plausible when comparing answers provided within six months of one another? This article explores the factors associated with short-term fluidity in Aboriginal identification. In order to do so, it uses Canadian data collected twice from among the same members of the defined “population of Aboriginal identity” over a six-month period, in 2006 and in 2011–2012. Close to a third of all Canadians who “identified” as Aboriginals in the Census long form or in the National Household Survey (NHS) changed their answers when asked the same question in the Aboriginal Peoples Survey (APS). Fluidity in identification depends on methodological factors such as mode of administration and question wording. It also depends on individual and contextual factors. Socioeconomic status and residence in an urban area or in specific regions of Canada are the main factors that differentiate the three groups analyzed here—the Fluid Indian/Métis, the New Métis and the New Indians—from the group that has a stable identification. In light of this finding, we think that statistics produced on Aboriginal peoples in Canada from the standard sources should be treated with some caution. Using the APS identification numbers, for example, instead of those of the Census/NHS would likely reduce the estimated differences between “non-Aboriginals” and “Aboriginals,” at least in terms of education.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".