Bibliographic record
Abstract
Immigration is a global trend, which increases the ethnic, racial and religious diversity in the immigrant-receiving countries. This diversity that immigrants bring is usually perceived as a threat to national security and social cohesion. In the face of these perceived threats, the term “integration” referring to commonality within diversity has come to the forefront as an ideal goal in the public debates on immigration. However, this dominant perspective on the issues of immigration orients us to understand the goal of integration from the receiving country’s perspective, approaching the integration of immigrants as a necessity for social cohesion and security of the host country rather than as a democratic justice problem. This dissertation provides a distinctive perspective on the issue of immigrant integration. As an interdisciplinary research project, it strives to accomplish this task by employing Axel Honneth’s recognition theory as an analytical tool to understand and criticize existing institutional and societal structures of integration in the host societies. In Part One, my basic argument is that immigrants cannot integrate to the overall society unless the recognition order of the host society provides normative conditions such as equal respect and esteem for its immigrants’ healthy self-realization. As opposed to the dominant approach, I propose to understand the ideal of integration as a concrete process, which is strongly related to the immigrants’ feelings of misrecognition and denigration. After articulating the advantages of employing Honneth’s recognition theory for the issue of the integration of immigrants; in Part Two, I consider the application of the theory to the specific experiences of Canadian immigrants. I present how economic integration mechanisms for immigrants in Canada may systematically devalue immigrant labor, transform their self-esteem, and as a result inhibit their integration into the host society. Specifically, I investigate several economic barriers specific to Canadian immigrants such as the non-recognition of foreign credentials, the lack of “Canadian experience,” limited English skills as the reasons for the higher rates of poverty and unemployment that many immigrants experience compared to their Canadian counterparts. Finally, through an application of Honneth’s recognition theory, I contend that in addition to improving state institutions to provide fair terms of integration to immigrants, we need to examine the economic and social barriers that immigrants are subject to in their search for meaningful, fair employment and social networks.
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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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".