A Guide for Foreign Trained Newcomer Architects: Identifying barriers to re-licensure in Canada
Bibliographic record
Abstract
Canada has a reputation of maintaining a positive outlook on immigration and prides itself on the belief that its doors are open to individuals all around the world. Canada’s government welcomes a diverse number of immigrants who have come for reasons including economic pursuit, family reunification, or to seek protection as refugees. According to 2017-2018 Canadian statistics, 80% of the nation’s population growth was due to the international migration of newcomer Canadians. Of those permanent residents admitted into Canada, 58% were of the Economic Class category (Hussen, 2018); who significantly contribute to the Canadian economy. As it pertains to this thesis, newcomers are defined as immigrants belonging to the Economic Class. Although it appears that migration might be open to all cultures, there is a hidden barrier that might prevent some immigrant professionals from migrating to Canada. Self-governing professional bodies, of disciplines such as architecture, engineering, law, nursing, and others, have systemic implicit biases that establish barriers and bar newcomer professionals from practice. This thesis investigates and unpacks the practices of these professional bodies, with a focus on architecture. While rules and regulations ensure the safety of both the public and the client, can self-governing bodies in Canada offer opportunities to professional newcomer architects to encourage, instead of deterring them from practice? Practicing architecture in Canada is mandated by the provincial self-governing body, the Ontario Association of Architects (OAA). This thesis investigates and compares the history, organizational processes, and public interface of the OAA to other self-governing professional organizations and recommends options that can be integrated into the practice.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".