Canadian immigrant guidelines on how to become productive members of society
Bibliographic record
Abstract
Purpose The purpose of this paper is to critically look at how immigrants to Canada are informed and educated about how to become productive members of society. The authors adopted a postcolonial framework to unveil the underlying assumptions embedded in the messages that are conveyed to “teach” and “prepare” immigrants for the Canadian workplace. In particular, the authors focus on non-white immigrants because they form the majority of immigrants to Canada and at the same time data show that they experience particular socio-economic obstacles in their settlement process that European immigrants did not. Design/methodology/approach The authors apply postcolonialism as the theoretical framework. This approach allows the authors to analyze the relationship between the local subject and the encounter with the non-local other, in this case the immigrant who is from a non-European background. The authors conduct a Foucauldian critical discourse analysis on selected texts that serve as information sources for immigrants. These texts include government documents, immigrant information brochures, and workplace information books and booklets. Findings The analysis shows ideological positions that reveal discursive messages representing the non-white immigrant in binary terms. Such immigrants are represented in opposing (and inferior) terms to the local (largely white) Canadian citizen. By adopting a postcolonial lens, the analysis shows that the messages to acculturate immigrants reveal assimilationist features. Research limitations/implications The authors acknowledge that the authors’ own personal socio-political, intellectual, and ideological locations influence the approach, logic, research process, and the interpretation of the findings. For future research, other textual sources should be analyzed with regard to the messages they convey to immigrants as a form of education to see what kind of acculturation is conveyed. Practical implications This paper sheds light on the necessity to develop policies that not only aim to acculturate immigrants using integration strategies but also to carefully communicate and educate newcomers through messages that that do not stem from colonial assumptions. Originality/value This research points out the taken-for granted and oftentimes invisible forms of discriminatory practices in the workplace that appear non-discriminatory on the surface but are rooted in colonial thinking. Consequently, the authors challenge “mainstream” management theories concerning diversity in the workplace by questioning the underlying messages portrayed to immigrants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.012 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".