Charting the Future of Forced Migration Research in Information Science: International Workshop in Washington, DC, United States, March 31, 2019 (Booklet)
Bibliographic record
Abstract
Information as a research object in the area of forced migration and vice versa, forced migration in the information science domain, is only slowly gaining attention and has not yet been addressed in an interdisciplinary research environment. Information science provides a perfect lens through which to examine a range of forced migration-related issues, practices, and methodologies. In the workshop, contributors and attendees will be able to tackle some of the following research questions: How can the information practices, spaces and environments of refugees be better defined and understood? How can information practices of refugees, and of the service providers that work with them, be steered to support the inclusion process? How do refugees deal with the disruption of knowledge during transitions? How can refugees’ information needs be better supported? And by whom? What role do information institutions and information professionals play in the forced migration context? What can they do more of, or better? Is a new framework necessary? Throughout the workshop, attendees will be able to examine these research questions, focusing on one of the two following themes: Information spaces of refugees navigating the information environments in new and/or transitional countries operational knowledge about information practices in different contexts making space for refugees evolving services provided by (public) libraries and other information professionnals in the context of forced migration Digitally-mediated environments of refugees information-related skills and strategies that facilitate access to information the role of social media and online spaces as sources of information and in creating and regaining a sense of place role and/or importance of access to ICT for refugees credibility and assessment issues; multilingual interactions; user-generated content.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.056 | 0.016 |
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".