Non-Disease Gendered Effects as a Point of Analysis in Pandemic Response: A Comparative Case Study of COVID-19 Response Measures in Canada and New Zealand
Bibliographic record
Abstract
The effects of the COVID-19 Pandemic on women extend beyond the disease itself and into the social and economic lives of many. This dissertation takes the form of a comparative case study and delves into the question of how well the response measures enacted in New Zealand and Canada reflect a thorough consideration of the gendered human security outcomes of the measures. As well, it assesses whether gender stakeholders were involved in the decision-making processes around policy and response. The dissertation analyses documents on the response measures and uses an intersectional approach based on criteria from each country's action plan on the Women, Peace, and Security Agenda. Overall, the results show that while mention of gender was present in the documents, it was not overwhelming and there remained many areas where it could have added significantly to the quality of the measures. Likewise, certain areas of the Women, Peace, and Security Agenda were severely underrepresented. Overarching areas of weakness include a lack of emphasis on prevention and relief and recovery. Specific weaknesses include measures that ignore the underlying gender inequity within the home and labour force, the absence of measures to aid sex workers, and a lack of recognition of the gendered dynamics often present in...
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".