Many indigenous people in settler societies like Canada, Aotearoa/New Zealand, the United States, and Australia live in urban centres (Australian Bureau of Statistics,
Bibliographic record
Abstract
Bureau, 2002). Of the nearly one million people in Canada who identified as Aboriginal in 2001 (over 3 % of the Canadian population), 49 % resided in urban areas (Statistics Canada, 2003a).(1) The corresponding figure in Aotearoa/New Zealand is higher where 83 % of people of Ma ori descent were living in urban areas in 1996 (Goodwin, 1997). The notable presence of indigenous peoples within the urban landscape contrasts with the low level of attention given to their citizenship pursuits. As Andersen and Denis (2003) argue in the Canadian context, the privileging of nation-based and land-based models of Aboriginal citizenship, normalised within federal-government discourse, has had the effect of marginalising urban Aboriginal communities. Growing urban indigenous populations present opportunities for economic and cultural growth as well as diversification in cities. At the same time, indigenous peoples face some acute cultural, social, and economic challenges such as disproportionate housing hardship in comparison with nonindigenous populations. Recent figures from Canada and Australia show, for example, that indigenous households in urban centres are much more likely than nonindigenous households to live in rental housing and in homes that are crowded or in need of major repair (Australian Bureau of Statistics, 2002; Statistics Canada, 2003b). Aboriginal homelessness in large Canadian urban areas ranges from 20 % to 50 % of the total homeless population (Privy Council
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".