The COVID-19 Gender-Responsive Policies in the Arctic (2020–2022)
Bibliographic record
Abstract
The study on the COVID-19 gendered policy responses in the Arctic aims to improve understanding of the impacts of the COVID-19 pandemic on women in the New Arctic at regional and local levels. The dataset provides information on a wide range of policy measures introduced by Arctic countries' governments to tackle the COVID-19 pandemic in order to promote sustainability in the region, as well as identifies gender-responsive policies. These policies directly address women's economic and social security, including unpaid care, female-dominated sectors of the economy, and violence against women. In addition, the dataset includes information about COVID-19 Task Forces, highlighting gender composition. This study followed the methodology developed by the United Nations (UN) Development Programme and UN Women, yet the novelty of this study is that it is designed to aggregate COVID-19 measures implemented by various levels of governance. As a showcase of different levels of governance, the dataset uses the examples of the selected study sites – Iceland, Russia, and the United States – to test a new approach. For the United States, it highlights measures at the state (Alaska) level and municipal level (cities of Anchorage, Fairbanks, Juneau, and town of Nome). For Iceland – the municipal level (city of Akureyri and town of Húsavík). For Russia, it covers the regional (Nenets and Chukotka Regions) and municipal (city of Naryan-Mar and town of Pevek) levels. In addition, the dataset includes national policy measures for Canada, Finland, Greenland (data for Greenland are currently not readily accessible; thus, the dataset provides information about policy measures for the Kingdom of Denmark), Iceland, Norway, Russia, Sweden, and the United States, as identified by the UN. Based on this dataset, the COVID-GEA project developed the Arctic COVID-19 Gender Response Tracker (COVID-GEA Tracker). The COVID-GEA Tracker is available here: https://www.arcticcovidgender.org/tracker The study is based on publicly available data, including official documents, and the UN COVID-19 Global Gender Response Tracker data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".