Bibliographic record
Abstract
This dissertation is an ethnography of Muslim believing, becoming, and belonging set in Dubai, amidst the aftermath of the Islamic Revival and Arab Spring. In this study, I explore how middle-class, migrant Muslim women grapple with Islamic theologies and ethical practices in everyday lives marked by the contingencies of living and laboring in Dubai as noncitizens. Compelled to accommodate changing economic markets, geopolitical alliances, and official stances on Islam in a place where noncitizens can never settle permanently, women develop ways of being Muslim which complicate earlier scholarly accounts of piety and subjectivity. I analyze how, as women cultivate their beliefs in God, destiny, and the afterlife, they falter and flourish through their attempts to be pious. I witness how they debate their role and responsibility in who they are, trying to shape themselves in different ways, sometimes becoming what they never anticipated. Throughout, I find, they form communities of belonging that spring from (and challenge) their existence as transient residents, connections that draw new constellations of commonality and selfhood. This believing, becoming, and belonging takes place in a context where the public, and often political, character of piety which thrived during the Islamic Revival (1970s onward) is undermined by state projects dedicated to depoliticizing Islam and society. Fashioned in response to the political uncertainties generated by the Arab Spring (2010-11) and one financial crisis after the next, these government policies shape the sites in which Dubai’s Muslims learn, work, live, and leisure. Intended as detours which help the UAE avert the strife other states experienced recently, these state-led agendas—which promote an ethos of individualism, self-help, tolerance, and positivity— configure the subjectivities and pieties of migrant Muslims in distinctive ways. For my interlocutors, being in Dubai also entails other kinds of detours— a move from a former self to someone radically new, or an extended diversion in a longer migratory journey. Analyzing these divergent detours, and the marks they leave on places and people, this dissertation offers a portrait of a global city, the UAE’s middle-class migrant experience, and the manifold forms Muslim piety takes after the Islamic Revival.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".