Employment-Related Information Experiences of Bangladeshi Immigrants in New York City
Bibliographic record
Abstract
This paper reports some key findings from a recent study on the employment-related information experiences of Bangladeshi immigrants to New York City. Using semi-structured interviews, the author interviewed 26 Bangladeshi immigrants in New York City between July 2024 and October 2024. In this work in progress paper, the author mainly discusses the information experiences of Bangladeshi immigrants to New York in terms of their personal networks, including friends, family, and co-ethnic community people. Les expériences informationnelles liées à l’emploi des immigrants·tes Bangladais·es dans la ville de New York RésuméCe travail en cours présente les résultats de 26 entrevues semi-structurées menées auprès d’immigrant·e·s Bangladais·es de la ville de New York, en se concentrant sur leurs expériences informationnelles liées à l’emploi. Il examine le rôle des réseaux personnels, tels que les ami·e·s, la famille et les membres de la communauté co-ethnique, dans la transmission des informations liées à l’emploi, avant et après leur arrivée. Dans cette étude, tandis que certain·e·s participant·e·s ont indiqué avoir reçu des renseignements et du soutien utile et opportun en matière d’emploi, d’autres ont eu des renseignements et des conseils obsolètes, vagues ou encore erronés. Les premières constatations de ce rapport mettent en évidence la complexité des expériences informationnelles des personnes immigrantes en lien avec l’emploi, ancrées dans des contextes culturels spécifiques. Elles offrent de précieuses perspectives pour les chercheurs·euses en science de l’information, en études sur la migration, en politique publique et d’autres disciplines connexes. Mots-clésExpérience informationnelle; interaction informationnelle humaine; sources informationnelles pour l’intégration
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".