Mapping of Population Diversity in Canada and Germany: Different Strategies, Similar Pragmatism
Bibliographic record
Abstract
The aim of this paper is to compare the respective approaches of Canada and Germany in statistically mapping population diversity and to offer possible explanations for the differences and commonalities observed. In order to investigate this, the paper takes into account the concept of ‘politics of belonging’ as a theoretical background and considers the functions of national statistics in categorizing different groups of people. There are different strategies of mapping population diversity and, inter alia, two models can be distinguished: while some countries explicitly include questions on elusive concepts of ‘origin’ in their population data collection, others refrain from doing so and instead derive different subgroups from information on citizenship and place of birth. Taking Canada as an example of the first group of countries and Germany of the second, and delineating recent changes within their respective strategies of measuring diversity within their populations, this paper argues that Canada and Germany converge towards a new pragmatism in the approaches of measuring diversity in population statistics.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".