Temporary Special Measures: A Possible Solution to Get More Women Into Politics
Bibliographic record
Abstract
In some ways, the Legislative Assembly of the Northwest Territories has been a trailblazer in terms of diversity of representation in Canada. Since full responsible government returned in 1983, a majority of its MLAs have been Indigenous, as have all but two of its premiers. Moreover, Nellie Cournoyea became the first Indigenous woman to become premier of a province or territory in Canada and only the second woman ever to hold a premiership in the country. In terms of electing women to the Assembly, however, it has lagged behind many other jurisdictions. Currently only two MLAs are women (10 per cent of the Assembly) and since 1999 the Assembly has only surpassed this number of women MLAs once – three (or 15.8 per cent in 2007). In order to become a more representative body, the territorial Assembly unanimously adopted a motion to ensure at least 20 per cent of MLAs are women by 2023, and at least 30 per cent of MLAs are women by 2027. In this article, the author explains the concept of temporary special measures to achieve this goal. She outlines the experience of Samoa, another small jurisdiction with Westminster roots in which women were substantially underrepresented in parliament, to demonstrate how the NWT might reach these benchmarks. She concludes by noting that temporary special measures are one way of increasing women’s representation in assemblies, but others may work as well depending on the jurisdiction’s political culture and institutions.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".