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Record W1989825485 · doi:10.11139/cj.28.3.621-638

From the Mouths of Canadian University Students

2011· article· en· W1989825485 on OpenAlexaboutno aff
Martine Peters, Alysse Weinberg, Nandini Sarma, Mary Frankoff

Bibliographic record

VenueCALICO Journal · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicLexicography and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceComputer-Assisted InstructionWorld Wide WebMathematics educationLinguisticsPsychologyMultimedia

Abstract

fetched live from OpenAlex

This article presents student perceptions about different types of web-based activities used to seek information for French language learning. Group interviews were conducted with 71 students in five Canadian universities to elicit data on their use of the Internet for information-seeking activities. These students use the Web for three main purposes: firstly, to expand their knowledge base by searching for information for language projects; secondly, to concentrate on form-focused activities by consulting online dictionaries or translation software; and finally, to organize their studies by consulting language course management websites. Our results are presented in a continuum of characteristics articulated by the students. Four continuums were identified: the first is goal-related (maintain/improve); the second is action-oriented (check/gather); the third involves the engagement of the students (receive/search), while the fourth one relates to the nature of the information (fact/culture).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.424

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0090.002
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.054
GPT teacher head0.207
Teacher spread0.153 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueCALICO JournalSame topicLexicography and Language StudiesFrench-language works237,207