Border narratives in Canadian social work: Neoliberal nationalism in the discursive construction of “citizen/Self” and “non-citizen/Other”
Bibliographic record
Abstract
This article elucidates how social work is not only constituted by cross-border processes but also constitutes the transnational processes of bordering within the territorial boundary of the nation-state. The analysis is drawn from a qualitative study of social workers who have worked with migrants without full immigration status in Toronto, Canada. Building on critical border scholarship that reconceptualizes borders as processes, I examine border narratives – a discursive-level operation of border making. I highlight how neoliberalism, one of the key technologies of contemporary transnational bordering processes, intersects and works together with nationalistic citizenship discourse, governing the discursive constructing of “citizen/Self” and “non-citizen/Other.” I call this governance at play neoliberal nationalism and demonstrate some of the ways that neoliberal nationalism works on, through, and within social workers to make sense of exclusionary and inclusionary practices towards migrants without full immigration status as they struggle to navigate a highly complex immigration system and funding structure as well as the effects of neoliberalism in their workplace. I demonstrate how social workers reproduce neoliberal logic and the hegemony of national citizenship even as they critique them, rendering it challenging to see their own complicity in the internal border making of the Canadian nation-state.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.046 | 0.053 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".