Strangers in the city: Spacing and social boundaries among accountants in the global city
Bibliographic record
Abstract
Abstract By conducting in‐depth interviews with 40 migrant and local accountants living and working in Dubai, the paper demonstrates how individual accountants draw on the global city as they make sense of and reconstruct professional identities and social boundaries of inclusion/exclusion. The analysis builds on Zygmunt Bauman's three social spacing processes: cognitive, aesthetic, and moral. The findings from lived experiences show that through cognitive spacing, the construction of other nationality groups of accountants as “strangers” is essential to the construction of participants' professional identities. Boundaries between groups of accountants are constructed in professional spaces (e.g., offices, teams, departments, sectors, firms, senior positions) through stereotype‐based identity work, stigmatizing accountants from different nationalities, as well as constructing cultures and qualifications as suitable or unsuitable for these spaces. In aesthetic spacing, boundaries and identities are constructed around the extent of freedom to be mobile, travel, and live pleasurably. This gives rise to the “tourist” and “vagabond” categories of accountants, constructing boundaries around who should be included/excluded in senior positions, based on the transitional power of their passports. In moral spacing, some accountants reflect and use their agency to transcend and resist boundaries constructed in cognitive and aesthetic spaces. They enact a moral self, where relationships with “stranger” accountants are based on moral responsibility and care for the Other. Attention to spacing processes in the global city demonstrates how individual accountants are active agents in reconstructing inclusion/exclusion boundaries and divisions in the profession. The findings indicate that if we are to foster a more open and inclusive profession, there is a need to consider spatial/spacing dimensions in both the city and the profession.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".