Constructing workplace subjectivity: exploring workplace learning of immigrant settlement workers in Canada
Bibliographic record
Abstract
This article explores the workplace learning of immigrant settlement workers (ISWs) at immigrant service agencies (ISAs) in Canada. Adopting a combination of governmentality and workplace subjectivity as its theoretical framework and institutional ethnography as its methodology, the study examines three forms of workplace subjectivity. First, constructive subjectivity is formed by incorporating racialized immigrants’ prior professional and linguistic skills into service delivery. However, the initial hiring intention is grounded in institutional governance, which deliberately prepares these workers for the knowledge of outcome measurement evaluation. Second, organisational training naturalises ISWs’ professional subjectivity to fulfil their apparatus role for the institutional regime. Lastly, cultural subjectivity manifests itself in two modes of paradoxes. The promotion of Eurocentric workplace knowledge assimilates ISWs’ behaviour, communication, and bodily comportment to the practices of neoliberal workplace value. Outcome measurement adopts culture-blind criteria, emphasising programme quantification while ignoring ISWs’ cultural identities in service delivery. In light of these findings, we argue that ISWs’ workplace subjectivities are purposefully coordinated by translocal governance ruling power that upholds funders’ requests for outcome measurement. The findings are significant in developing ways of understanding and exploring ISWs’ learning agency in ISA workplaces by capturing and emphasising their voices.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.028 | 0.021 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".