Academic Career Paths in Linguistics: A Report on the CLA Questionnaire
Bibliographic record
Abstract
Abstract This paper presents the results of a survey conducted by the CLA in 1996-1997. Mail-in questionnaires were completed by 110 Canadian linguists from all regions of the country, 71 of whom (65%) were female, and 39 (35%) male. Following an overview of the literature dealing with women in academia in general and linguistics in particular, a summary of the background and status of the respondents is given. The results are presented thematically, exploring (i) the division between teaching, research and committee work; (ii) mentoring; (iii) financial support; (iv) the relative prestige of sub-disciplines. Of note is the fact that SPSS analyses show very few significant differences when sex was taken as a variable. The quantitative results provide a snapshot of linguistics in Canada in the late 1990s, and coupled with the numerous comments provided by respondents, point to issues that need to be addressed by the Canadian Linguistics Association. These are summarized in the Conclusion as a series of recommendations to the Association.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".