Bibliographic record
Abstract
By the time I completed fieldwork in Qatar in 2016, the Msheireb project that aimed to redevelop the heart of Doha was nearing completion of its first phase. The way this development project unfolded during this time was indicative of broader negotiations of Qatari cultural brokers with ideas of indigeneity and expertise: the project was rebranded from Dohaland, “The Heart of Doha,” to Msheireb (after the local wadi) in the first half of 2011, while the iconic Al Kahraba Street—the “spine” of the old city and the first street to be electrified in Qatar—was referred to in architectural notations as the “Champs-Élysées” of Doha. The politics of indigeneity of place-making in these negotiations reflected an unresolved unease with the cosmopolitan nature of expertise. This tension was related not necessarily to the ingenious remixing of traditional and modern concepts of cultural and environmental sustainability, but rather to the active erasure of foreign expertise. We see this, for example, in the active rebranding of the I. M. Pei's Museum (in reference to the Chinese American “starchitect” who designed it) in order to take its rightful “local” name, the Museum of Islamic Art. While these efforts would suggest an intention to localize expertise and build local capacity as part of national objectives, I argue in this essay that this mastering in fact obscures local expertise by dissociating it from the cosmopolitan context in which knowledge production is negotiated in Qatar.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.060 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".