Linguist is as Linguist Does: A Comparative Study on the Employment and Income of Graduates from Linguistics Programs in Canada
Bibliographic record
Abstract
This study attempts to answer a perennial question asked of and by every student of linguistics: ‘What can you do with this degree?’. We address the question through an in-depth analysis of administrative and tax data from Statistics Canada (2009–2018). Specifically, this article (i) maps out educational and employment pathways of linguistics graduates in Canada, (ii) compares their earnings to graduates from other ‘competitor’ programs that future linguists consider as viable alternatives, and (iii) verifies the range of careers advertised by linguistics departments against the reality of the industries in which graduates from those departments are employed. These findings enable us to draw conclusions about the optimal and suboptimal educational and career pathways that involve a linguistics degree. Linguistics graduates tend to earn less than their peers in comparable programs, unless they pursue a lengthy educational path. The findings also point to a partial mismatch between potential careers advertised by Canadian linguistics departments and actual areas of employment after graduating with a linguistics degree. We provide suggestions for linguistics departments on how best to align the policies and practices of these programs with the ground truth of the labor market.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".