Bibliographic record
Abstract
This study explores the information behaviour of staff working in postsecondary education who write work emails in second-language French (L2). The article is inspired by existing research in public administration and translation. An exploratory study is conducted through the use of semi-structured interviews. Three participants (n=3), bilingual in English and French, shared their experiences preparing work emails in French and discussed their consultation of internal, online, and social information resources. The findings highlight several themes: linguistic identity, reliance on prior L2 knowledge, use of online tools like DeepL and WordReference, and consultation with Francophone colleagues for complex questions. These themes help to shape a proposed framework based on Byström and Järvelin's (1995) model, elaborating a process of information seeking according to perceived task complexity. The model also invokes Zipf’s Principle of Least Effort, demonstrating participants' interest in minimizing time and effort in consulting information resources, while prioritizing accurate email content. Overall, the study illuminates how individuals in postsecondary roles navigate writing work emails in a learned language, outlining their information behaviour and the relationship between internal and external information resources.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".