Refugee Youth Leverage Social, Physical, and Digital Information to Enact Information Literacy
Bibliographic record
Abstract
A Review of: Lloyd, A., & Wilkinson, J. (2017). Tapping into the information landscape: Refugee youth enactment of information literacy in everyday spaces. Journal of Librarianship and Information Science. Advance online publication. http://dx.doi.org/10.1177/0961000617709058 Abstract Objective – To describe the ways in which refugee youth use everyday information to support their learning. Design – Photo voice technique, a process by which the population under consideration is provided with cameras and asked to visually document an aspect of their experience. Setting – Social agency in New South Wales, Australia Subjects – Fifteen 16-25 year old refugees resettled from South Sudan or Afghanistan Methods – Three workshops were conducted. In the first, participants learned how to use the cameras and the protocols for participation. Between the first and second workshops, participants took several photographs of places, sources and types of information that were personally meaningful. In the second workshop, participants were first split into small groups to share and discuss the five images that they selected as their most important information sources and later reconvened as a large group in which participants again shared and discussed their images. In the third and final workshop, the authors shared their findings and analysis with the participants and invited discussion. The authors analyzed both photos and group transcripts from the workshops using Charmaz’s constant comparative method. Main Results – Refugee youth use digital, vernacular, meditational, and visual literacies in everyday settings in to order to understand and create their new information landscapes. Information literacy enactment is agile and responsive to context. Conclusion – Engaging with digital, vernacular, and visual information in a variety of contexts is central to how young refugees (re)form their information landscapes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".