Search Language Selection in Context: the information practice of established active multilinguals in Southern Ontario, Canada
Bibliographic record
Abstract
Established active multilinguals use non-English languages in their information practice, even in English majority locations like Southern Ontario, Canada. This thesis investigated the reasoning highly fluent multilinguals use when selecting languages in their daily life information seeking. Twenty multilingual participants who regularly use sixteen unique languages (including twelve participants fluent in French) were recruited and interviewed using Marian et al.,’s (2007) Language Experience and Proficiency Questionnaire (LEAP-Q), which was expanded with novel questions to interrogate information practices. A novel search journal task was additionally included to stimulate practices in a culturally dis-fluent context. Five key language selection rationales were identified following Braun and Clarke’s (2006) thematic analysis: Unequal Resource Representation, Translation Labour Reduction, Social Resources, Self-Management, and Programmed Selection. Multilinguals reported how the context of each language shapes its use in both the online and offline environments of information practice. The unequal representation of information resources means that one language may have more accessible resources, or more culturally or demographically relevant resources than others. The worldwide influence of English, especially in academic and scientific publishing, continues to drive language selection especially in online information seeking. Speakers’ experiences, habits, and individual fluencies with language varieties, registers, writing systems, and topic-specific vocabularies influence their language selection. A language may be selected to avoid the effort needed to produce a translation or risks arising from misunderstanding, to engage in new or established relationships with other multilinguals, to maintain fluency in lesser-used languages, or to enjoy the aesthetic and emotional associations with a language. Multilinguals may also consider governmental obligations for French service provision, and workplace policies when selecting languages. Some decisions are controlled externally, as other people and corporate algorithms send information in languages determined by independent decision-making processes. Overall, this work highlights the multifactorial nature of language selection in multilingual information practice and makes novel observations about the effects of corporate control over how languages are presented online. Methodologically, it develops a novel tool for interrogating search language rationales and the novel Language Selection Rationale model to explain search language decision-making in future studies of multilingual information practice.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".