ICT‐mediated diaspora studies: New directions in immigrant information behavior research
Bibliographic record
Abstract
Abstract A growing and sizeable area of study within information behavior research focuses on the information needs and behaviors of immigrant populations (see Chu, ; Fisher, Durrance & Hinton, 2004; Caidi & Allard ; Srinivasan & Pyati, ). Some of the unique needs of these populations include information to aid with coping skills and social inclusion, as well as culturally specific information resources. Moreover, immigrant communities have information networks that span national boundaries, which affects their needs and uses of information and communication technologies (ICTs). This panel will focus on the role of ICTs in mediating the information environments of immigrant and diasporic communities. While focusing on how ICTs mediate immigrant information needs, this panel also contextualizes immigrant information behavior research within globalization and diaspora studies. For instance, the concept of “e‐diaspora” is a term gaining in popularity, but rarely invoked in relation to immigrant information behavior research. How do new media technologies mediate and influence the information needs and behaviors of immigrant populations? Are localized immigrant information needs mediated by diasporic information sources? The panelists will focus on ICT‐mediated services for immigrant populations within the context of both local and global information environments. Questions addressed include: In what ways do diasporic information environments shape local immigrant information needs and their social inclusion into the host society? How does the “digital divide” manifest itself in studies of ICT‐mediated immigrant information behavior? The issues addressed by the panel are both timely and critical as evidenced by the ongoing debates in Europe, North America and elsewhere on immigration policy, on integration and identity, and the role of ICTs in a globalized world. This international perspective will be reflected in the composition of the panel.
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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 | 0.024 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".