A “Leaky” Pipeline and Chilly Climate in Archaeology in Canada
Bibliographic record
Abstract
This article quantifies the rate at which women archaeologists are present and retained in university departments. Drawing on publicly available data, we examine gender representation in (1) doctorates earned between 2002–2003 and 2016–2017; (2) Social Sciences and Humanities Research Council (SSHRC) grant applications and awards at the doctoral to senior levels between 2003 and 2017; (3) tenure-stream faculty at Canadian universities in 2019; and (4) placement of Canadian PhDs in the United States. These data demonstrate that women today represent two-thirds of all Canadian doctorates in archaeology, but only one-third of Canadian tenure-stream faculty, although not all archaeologists choose an academic career. In the last 15 years, women with Canadian PhDs have been hired into tenure-track positions in Canada at rates statistically lower than men, but at higher rates in the United States. Women apply for SSHRC archaeology grants in equal proportion to their presence, but men are awarded at a slightly higher rate. We end by discussing the possible reasons for this gendered attrition, including a “chilly climate”—that is, subtle practices that stereotype, exclude, and devalue women, as well as inhospitable working environments, particularly for primary caregivers. We warn that the current COVID-19 pandemic is likely to exacerbate these existing inequalities.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| 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".