Doktor Adaylarının Çok Dilli Ortamlarda Yayın Amaçlı Fransızca Kullanma Motivasyonları
Bibliographic record
Abstract
In academia, publishing in English-medium journals has become the norm and multilingual scholars, whether they work in English-dominant settings or not, experience immense pressure to publish in English.However, we find, increasingly, a discourse surrounding the importance of promoting multilingual publishing practices and some researchers have examined experienced scholars' beliefs and practices in relation to English for research publication purposes (ERPP).Despite these contributions, we currently have a narrow understanding of emerging scholars' practices and beliefs.The present case study investigated two doctoral French-English multilingual scholars' publication practices studying in a multilingual Canadian city.We focused on the factors that influenced their choice of publication languages (French and English) and their multilingual citation practices.Drawing on interview data, on-line questionnaire data, and the participants' actual French and English publications, we found that their future professional goals and an understanding of their readership mediated their language of publication choices.Findings are discussed from Bourdieu's (1994) Social Theory framework.
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 | 0.004 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.011 |
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".