Being a Creative and an Immigrant in Montreal
Bibliographic record
Abstract
Work on creative careers has focused on the main national populations, while little research has addressed the situation of artists and creators of immigrant origin or different ethnic groups to determine whether they have the same access to work and employment rights. To respond for a call for research on different ethnic groups in the cultural sector, or the ethnic consequences of the individualization of careers, we therefore undertook research on the creative careers of immigrants in Montreal. We were interested in how they emerged as an artist, how they developed their careers, the access and rights they have in terms of support to their career, as McRobbie seems to indicate that ethnicity adds its “own weight to the life chances of those who are attempting to make a living in these fields. We found that these immigrant artists consider their main difficulties to be the lack of social networks, access to various forms of support to compensate for financial risks and difficulties in finding a job. We conclude with a few suggestions: measures to facilitate networking for immigrants, more training and information on government programs, mentoring support, as well as the support from community organizations, associations, and programs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".