Do I not belong here? Navigating social work as a racialized professional within the Moroccan diaspora in Catalonia
Bibliographic record
Abstract
This article examines the recognition and visibility of racialized professionals in social work while also analyzing the professional practice of a racialized worker within the context of Catalan social work. The aim is to capture the elements that have facilitated or limited the process of social and educational intervention with users who are also racialized primarily from the Moroccan diaspora. Building on previous studies such as Badwall’s (2015) work in the Canadian context, this article highlights how racialized social workers are often perceived by their work environment as less competent. Using autoethnography as a research methodology, this study reviews professional practice to understand the factors affecting social and educational interventions. The findings reveal a process of labor ethnicization that negatively impacts the perception of professional competencies, often discrediting their interventions. Despite this, it is evident that establishing connections with users becomes a key strategy to enhance proximity and effectiveness in professional practice. The article concludes by calling for the development of new paradigms in social work that consider the processes of racialization and the experiences of immigrant-origin professionals as tools to promote more inclusive practices.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".