Categories as learning practice: navigating contested belonging along transatlantic mobile trajectories
Bibliographic record
Abstract
Although mobility-related categorization processes are central to migration studies, the ways that mobile populations understand, adapt, or contest them remain understudied. To trace such interpretations across both space and time, this paper explores a migrant trajectory that first crossed national borders within Africa before continuing to Brazil and later proceeding to Canada. The research combines ethnographic insights with the autobiographic reflections of one protagonist, whose perspectives and experiences move between different places, countries, institutions, people, and critical events. Following that individual’s learning processes, this article traces which categories were meaningful in the context of origin, how these changed in the interaction with different authorities, how transformative events played into valorizations, and which signs of categorical dissolution were recognizable during these trajectories. A biographical learning perspective sees not only the aspirations and the ideals but also the pragmatism and skepticism around the impact of mobility-related categories change along such journeys.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".