Bibliographic record
Abstract
The petroleum-rich states of the Arabian Peninsula comprise one of the principal transnational destinations for the global movement of labour. In the Gulf States, much of that labour force comes from South Asia. Legions of unskilled male labourers are typically housed in labor camps, a nomenclature that masks a wide variety of both formal and informal accommodation that, in spatial terms, is a fundamental mechanism for the social segregation of this foreign labor force from the citizenry. Building upon recent fieldwork in Doha, Qatar, this paper examines the myths and narratives that proliferate amongst the South Asian men in these labor camps – men who, often despite years of experience in the Gulf States, typically have little or no interaction with the native citizenry. This paper suggests that these myths and stories can be understood as instruments of governance in that they portray the collectively-established boundaries of appropriate behavior in a culture foreign to these unskilled laborers. A close analysis of the content of these myths and rumors, however, also helps us grapple with the connections and contradictions between power, race, class, ethnicity, nationality, religion, gender and sexuality in the extraordinarily heterogeneous context of the contemporary Gulf State. As such, the analysis not only sheds light on the experiences of these foreign men and women, but also their collective understanding of that experience and of the society that hosts them.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.036 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".