Towards a Definition of Multilingual Information Literacy (MLIL): An Essential Skill for the 21st Century
Bibliographic record
Abstract
This article reports on an exploratory study that examined bilingual/multilingual university students’ perspectives on how language affects their information searching and use. The study also examined instruction librarians’ perspectives on information literacy instruction in general and their approaches in providing information literacy instruction to international students and English as a Second Language (ESL) students. A qualitative research approach using focus group discussions and semi-structured interviews was used in the study. Nineteen (19) international and ESL students participated in the discussions while 8 instruction librarians were interviewed. Fifty-six (56%) of the students were aware of information literacy instruction as a service that was offered by the University library but only 37% had used this service. Only one of the librarians had had a significant encounter where language issues closely intersected with information literacy instruction. This study makes a connection between language and information literacy and reports on perspectives from both librarians and students’ point of view. While proposing a possible working definition of Multilingual Information Literacy (MLIL), the study makes the case for MLIL as a necessary skill for the twenty-first century. The study also proposes ways in which Library and Information Science (LIS) professionals could be involved in promoting and enhancing multilingual information literacy and further suggests Specialized Information Literacy Instruction (SILI) and Personalized Information Literacy Instruction (PILI) as suitable models for providing instruction to Limited English Proficient (LEP) users.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".