Bibliographic record
Abstract
The Toronto region receives one‐quarter of new immigrants to Canada and they become widely dispersed throughout the metropolitan area. Most immigrants arrive with language, social and cultural needs, creating demand for social services from existing agencies. ‘How can agencies choose locations that meet the needs of new immigrants?’ is the central focus. The results of a study in Metropolitan Toronto of 68 nonprofit agencies that provide a variety of settlement services for immigrant and refugee women are discussed. Immigrant and language groups and the agencies serving them are mapped; the locations of agencies are evaluated. While service agencies are responding to the arrival of new groups and the spatial dispersion of new immigrants, more services in the northern portions of the study area are required. The spatial dispersion of some language groups means that they have poorer access to services than groups that are concentrated in the traditional immigrant reception area. La région de Toronto accueille le quart des immigrants au Canada, et ceux‐ci sont dispersés dans l'agglomération torontoise. La plupart d'entre eux ont des exigences linguistiques, sociales et culturelles qui augmentent la demande en services sociaux dispensés par les organismes en place. Ce document porte essentiellement sur la façon dont ces derniers determinent les lieux de prestation de services qui répondront le mieux aux besoins des immigrants. II est également question des résultats d'une étude menée dans la communauté urbaine de Toronto auprés de 68 organismes à but non lucratif offrant un éventail de services d'établissement pour les immigrantes et les réfugiées. Les immigrants et les groupes linguistiques, ainsi que les organismes qui les servent, y sont répertoriés géographiquement. La localisation de ces organismes fait aussi l'objet d'une évaluation. La plupart répondent déjà aux besoins des nouveaux venus et tiennent compte de leur dispersion mais, selon cette étude, il faudrait plus de services dans le nord de l'agglomération torontoise. En raison de cet éparpillement, certains groupes linguistiques ont plus difficilement accès à des services que d'autres qui se trouvent dans les zones d'ancrage habituelles.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".