Bibliographic record
Abstract
Abstract It was argued Chap. 1 that the constrained agency of elite migrant-artists has been an under-researched area. This means that there are, in fact, many unanswered provocative questions about the life and work of global elite migrants. While they belong to the category of informants whom scholars view as ‘challenging’. They are highly visible, dependent on their networks and, therefore extremely vulnerable because of their potential exposure to the public and also because of severe network sanctions. They are both privileged and vulnerable. And as noted by the famous French novelist of the nineteenth century Honoré de Balzac, it is not easy to describe in one word ‘the splendor and miseries’ of someone so controversial. Therefore, the question that I would like to ask in this chapter is what would be the best way to study the lives of global elite migrants and the ontogenesis of their migrant agency with the purpose to make their voices heard and their ‘splendors and miseries’ visible. What would be the best way to think about them as professionals, migrants and real people, with all their social skills, ambitions, moments of success but also fears and insecurity? The answer is interpretive biography. Here in this chapter, I, therefore, introduce the method of interpretive biography, explain its nuances and analytical procedures, and justify its application to my case.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".