Persistent Sacrifice: The 'Sacrificial Lamb' Effect, Women Candidates, and Underrepresentation in the 44th Parliament
Bibliographic record
Abstract
In August of 2021, Canadian Parliament was dissolved and the 44th federal election was called. Its result was a Parliament that was only marginally more representative of female and gender-diverse candidates than the last. This is in part due to the ‘sacrificial lamb’ effect identified by Melanee Thomas and Marc-Andre Bodet, which highlights the persistence of parties in running women and gender-diverse candidates in unwinnable or ‘swing’ ridings – such that they become ‘sacrifices’ and are destined for failure. Previous research has confirmed the presence of the ‘sacrificial lamb’ effect across several elections. I examine specific incidents of gender-diverse candidates being run in the ‘stronghold’ riding of another party, as well as the slates of candidates in several ‘swing’ and ‘stronghold’ ridings across the country. Further, I consider the likely implications of the ‘sacrificial lamb’ effect and the general underrepresentation of female candidates in the 44th Parliament, as well as the extent to which the ‘sacrificial lamb’ effect may have compromised the freeness and fairness of the 2021 campaign. I conclude that the ‘sacrificial lamb’ effect continued in full force throughout the 2021 campaign, which is likely to have a detrimental impact on the representation of women’s interests, specifically as they pertain to the pressing women’s issues of today.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".