1 A PAN-CANADIAN SETTLEMENT VOICE: CONSTRAINTS AND OPPORTUNITIES
Bibliographic record
Abstract
Each year, more than 200,000 persons arrive in Canada seeking to make a new home here. They form part of a growing transnational flow of individuals, goods, services, information, and ideas that characterize a globalized world. Canada needs these newcomers and has a strong interest in ensuring their settlement, including labour market integration, access to education, and civic participation. Newcomers also bring with them new perspectives on governance and social issues. As they move to claim social and political citizenship in Canada, thereby placing their own demands on the system, they challenge the nature of existing institutions and policies. Under conditions of globalization, governance occurs at various levels, in the economic marketplace and in society, within states and transnationally. Power is fragmented and reconstituted in many different centres. In Canada, a three-tier structure of federal, provincial, and municipal governments may be inadequate for meeting the complexities of contemporary immigration. Similarly, existing nongovernmental voices on issues of immigration and settlement may lack the resources to master these complexities.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.055 | 0.014 |
| Scholarly communication | 0.025 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 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".