Dispersion and Polarization of Income among Aboriginal and Non‐Aboriginal Canadians*
Bibliographic record
Abstract
Cet article pose trois questions: 1) Pourquoi étudier les inégalités entre les autochtones? 2) Quel est l'écart entre les salaires et les revenus de la population canadienne en général et ceux des différents peuples autochtones? et 3) Jusqu'à quel point existe‐t‐il des inégalités entre les peuples autochtones ainsi qu'entre la population autochtone et la population non autochtone? Cet article montre une tendance générale de l'augmentation des disparités mesurées ainsi que de la polarisation des revenus chez tous les groupes autochtones compara‐tivement à la population non autochtone. Pour ce qui est de l'inégalité entre les groupes autochtones, les Inuits se classent au sommet de la pyramide, suivis des Indiens inscrits, des Indiens non inscrits et, finalement, des Métis. This article addresses three questions: 1) Why study intra‐Aboriginal inequality? 2) What is the gap in wages and income between the general Canadian population and the different Aboriginal peoples? and 3) How much inequality exists within the Aboriginal groups and between Aboriginal groups and the non‐Aboriginal population? The article points to a general pattern of increase in measured disparity and polarization in income for all Aboriginal groups in comparison to the non‐Aboriginal population. In terms of intra‐Aboriginal inequality, Aboriginal groups rank from Inuit at the high end, through Status Indians, to non‐status Indians and, finally, to Métis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".