Analyzing The Harper Government's Decision Making Process on Building and Funding the Canadian High Arctic Research Station
Bibliographic record
Abstract
The objective of this Capstone project is to analyze the decision-making processes of Prime Minister Stephen Harper’s government with regard to its Northern vision and of the establishment of a Canadian High Arctic Research Station (CHARS). Investigation of policy that supports this Northern vision will be reviewed throughout this Capstone to understand how the Harper government is implementing its announcements. The importance of the Arctic research station (CHARS) will be discussed throughout; specifically, emphasis will be placed on the changing environment in Northern Canada that has resulted from climate change. Global warming is causing changes to the arctic landscape, allowing once largely inaccessible Northern maritime passages to become accessible year-round. Additionally, there is currently no active body in Northern Canada that monitors or enforces Canadian claims to sovereignty over its Northern borders. These are significant issues that may influence the decision-making process of the Harper government while addressing the CHARS project. This Capstone project will emphasize the significance of the Canadian High Arctic Research Station (CHARS) and the government’s approach to its implementation. Specifically, strategic investment in the CHARS will help demonstrate Canada’s affirmation of its Northern sovereignty. Additionally, it will demonstrate that this project is essential to future investment and development in Northern Canada. The CHARS initiative will be scrutinized through the employment of decision-making models; these models will assist in the investigation of the decision-making process that is currently being pursued by the Harper government. In addition, data from investigation and literature review of the CHARS project and current Northern development in Canada will further emphasize the importance of the decisions currently being made surrounding Northern development. The inability of the federal government to address the implementation of the CHARS in a swift manner will be discussed throughout the paper, again with emphasis placed on the decision-making models. Using this type of analysis, one will understand why a project that has been promoted as integral to the future development of Northern Canada1 has been confronted with significant delays in its implementation. Throughout the analysis, a qualitative review of gains, losses and barriers to implementing the CHARS will be presented and investigated. Ultimately, this Capstone concludes by proposing suggestions through the lens of the decision-making model with respect to consultation, communication and implementation for the Canadian Federal Government. This will enhance the readers’ understanding of how to move forward more effectively in the development of the CHARS in Canada’s North.
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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 | 0.064 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.023 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".