History-based Explanatory Framework for Procreative Behaviour of Aboriginal People of Canada
Bibliographic record
Abstract
The transition from traditional high to modern low fertility is in the forefront of empirical and theoretical investigations in contemporary aboriginal demography. The challenging question therein remains why its fertility has started to decline a century or so after the rest of Canada, and why it continues to trail the latter by a considerable lag. The objective of this paper is to present a history-based explanatory framework of the childbearing behaviour of Canadian aboriginal peoples, as it has evolved over time from the very first contact with Europeans to our day. Turning to existing theories for possible elucidation of these idiosyncrasies, we find that while accounting for certain aspects, they leave others unexplained. History provides a more satisfying explanation when we cast an eye not on the “abstract” population, “ideal-type” or what we today like to call “model”, but on real population, or family thereof, in its spatio-temporal context. The “between-two-cultures” paradigm presented here, based on ethnocentricity and dependency, could be seen as a an explanatory paradigm of competing forces on the Canadian aboriginals: on the one hand, those pushing toward modern norms of childbearing; and on the other, traditional values and structures, as shattered as they are, with their pro-natalist ideologies resisting normative changes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".