Review of “White-Collar Blues: The Making of the Transnational Turkish Middle Class”
Bibliographic record
Abstract
Imagine yourself as a member of the transnational Turkish corporate class. You earn well—you cracked your country’s top salary decile before you hit 30—and you know the right things to spend your money on (You learned this on your family vacations to Miami as a kid and your semester in Europe as an undergrad). Eyebrows raise when you mention your employer in casual conversation; the name resonates not just in Istanbul but in New York and London, and this pleases you (and your parents). You may even live in New York yourself, or at least you travel there for work (in business class, of course, the same way you flew to the Maldives last winter, though not without your work laptop). You got to where you are because you worked hard in school, at your top-six university, naturally, where you cultivated the cosmopolitan cultural capital to appeal to professional gatekeepers, but even before that—going to school after school to learn the test-taking techniques for qualifying exams that would determine what you would be permitted to study and the life you were permitted to live. You worked hard—always—and you qualified for that life. Now you’re living it. And you’re miserable.
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 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".