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Record W2027294447 · doi:10.1145/2132176.2132276

Multi-lingual information access tools

2012· article· en· W2027294447 on OpenAlex
Peggy Nzomo, Victoria L. Rubin, Isola Ajiferuke

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings of the 2012 iConference · 2012
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceCross-language information retrievalThe InternetWorld Wide WebTest (biology)Information accessInformation retrievalFirst languageNatural language processingArtificial intelligenceLinguisticsQuery expansion

Abstract

fetched live from OpenAlex

This research presents the results of a case study on potential users of Cross Language Information Retrieval (CLIR) systems --- international students at a Canadian University. The study is designed to test their awareness of Multi-Lingual Information Access (MLIA) tools on the internet and in select electronic databases. The study investigates how non-native English speakers cope with language barriers while searching for information online. We advocate for designing systems that incorporate CLIR options and other MLIA tools to support users from diverse linguistic backgrounds with varying proficiency levels.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.012
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.314
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it