Regional Policy Agglomeration: Arctic Policy in Canada and the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Regional policies addressing urban policy, rural policy and policies with specific regional targets tend to evolve from the consideration of disparate issues that impact the designated region rather than as co-ordinated strategies. We label this aggregation of disparate policies as policy agglomeration. We examine this phenomenon for domestic aspects of Arctic policies in Canada and the United States. Arctic policy in each setting is comprised of a diversity of policy components with limited policy targeting for the Arctic region or populations. The greater targeting of Canadian policies with respect to both place and indigenous populations is explained by institutional and political factors. Acknowledgements We thank Courtney Munson for invaluable research assistance and Josh Sapotichne and anonymous reviewers for helpful comments. Funding for this research was provided to the University of Washington by the National Science Foundation under grant no. OPP-0219543 under a project co-directed by Bryan D. Jones and Peter J. May. The findings of this research are not necessarily endorsed by the National Science Foundation or the University of Washington. Notes Peter J. May is Professor of Political Science at the University of Washington and a faculty associate of the Center for American Politics and Public Policy. His research addresses policy design and implementation. Bryan D. Jones is Donald Matthews Professor of Political Science and Director of the Center for American Politics and Public Policy at the University of Washington. His research addresses policy dynamics and decision-making. Betsi E. Beem, Emily A. Neff-Sharum and Melissa K. Poague are graduate fellows of the Center for American Politics and Public Policy at the University of Washington where each is completing a PhD. This discussion is based on a review of the issues raised in congressional hearings, conducted in 1982, prior to passage of the Arctic Research and Policy Act of 1984. The Canadian Polar Commission Act of 1991 defines "polar regions" in relation to Canada as including all areas north of 60 degrees north latitude and all areas north of the southern limit of the discontinuous permafrost zone. To assess whether this difference biased our findings, we compared mean policy centrality scores for the US data prior to 1996 with US data that are limited to the period of the Canadian statutes, 1996–2002. We failed to detect a difference in the mean centrality scores for these two sets of data (t-test = − 1.10, p = 0.27). This suggests that the different periods are comparable with respect to the key variable of interest in this study. The percentage of the statute devoted to Arctic considerations was estimated by coders, rather than calculated by counting lines or words. Any such count would have been problematic because of differences in the construction of statutes. Another way to measure this difference is to compare the respective distribution of centrality scores that are shown in Figure 1. These distributions also differ (Chi square = 9.78, p = .02). The mean centrality scores and one-tailed t-test for scores when comparing Canada and the United States respectively for environmental policies are 2.40 and 1.44 (t-test = 1.78, p = .05) and for development policies are 2.29 and 1.30 (t-test = 1.58, p = .08). The mean centrality scores and one-tailed t-test for centrality scores when comparing Canada and the United States respectively for human services policies are 1.79 and 1.45 (t-test = .95, p = .18). Another way to measure this difference is to compare the respective distribution of centrality scores that are shown in Figure 2. These distributions also differ (Chi square = 10.51, p = .02). Additional informationNotes on contributorsPeter J May Peter J. May is Professor of Political Science at the University of Washington and a faculty associate of the Center for American Politics and Public Policy. His research addresses policy design and implementation. Bryan D. Jones is Donald Matthews Professor of Political Science and Director of the Center for American Politics and Public Policy at the University of Washington. His research addresses policy dynamics and decision-making. Betsi E. Beem, Emily A. Neff-Sharum and Melissa K. Poague are graduate fellows of the Center for American Politics and Public Policy at the University of Washington where each is completing a PhD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it