All things ethnic : comparing ethnicity in the official statistics of Canada and New Zealand
Bibliographic record
Abstract
This thesis provides an historical comparison of the ethnic questions in the censuses of Canada and New Zealand. The comparison spans six census years, selected to provide the best examples of changes in the respective censuses since World War II. Certain critical aspects are examined in detail, including the question wording, response categories, helpnotes, selected coding procedures, and multiple responses. Research material for this thesis was obtained from published and unpublished material from both Statistics Canada and Statistics New Zealand. Sociological definitions and theory relating to ethnicity are investigated, and these issues are related back to the difficulties in developing an ethnic question for Censuses. The 'race' component of census ethnic data is noted, and the possible explanation of the 'census as fossil' is offered. The tensions between the visibility of the ethnic question and the invisibility of ethnic classification systems are also explored. Censuses of Population, and especially the ethnic questions in censuses, are developed by official statisticians in the context of contemporary cultural norms and values. To that end, the economic and social histories of Canada and New Zealand are reviewed to provide a background for the discussion of each ethnic question. Where pertinent the comparison includes Australia, United Kingdom and the United states. The main differences between New Zealand and Canada are found to be in population composition (differences in the indigenous population, as well as the anomaly of the French in Canada) and the legislative requirements for ethnic data. The differences in the ethnic census data are discussed in this context, especially the fact that the New Zeeland question is a cultural affiliation measure, whereas the Canadian 'ethno-cultural' questions relate to ancestry, race and visible minority status. All census data are reliant on the goodwill of the public. If census ethnic data are to remain credible, official statisticians must continue to seek to understand the meaning and impact of census ethnicity questions, and must develop ways of collecting and disseminating the data that satisfy the needs of the users, producers and suppliers of the data. This thesis seeks to contribute to this endeavour.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".