Analysis of economic development of the Arctic regions of Canada
Bibliographic record
Abstract
Abstract The purpose of this article is to create a multi-level (in our case two-level) model that will be able to integrate models of different levels of a single socio-economic system. The multiple model is one of the possible approaches to the analysis of social and economic development of the Arctic region. The main feature of multiple models is that endogenous variables of a lower level are seen as exogenous variables of the following level. The focus of the article is to create a two-level model of the economic growth of the Arctic regions based on the example of Canada. In order to analyse the Arctic territories of Canada it was decided to use a functional approach with the model of autoregressive distributed lags (ADL – model). The general concept of the organization and development management of the Arctic territory of Canada is to represent the Arctic area as a set of target subspaces. For the purpose of target management such subspaces can be united flexibly in a form of an interactive network. This type of representation of the Arctic territory of Canada can be regarded to as subspace approach. This approach was selected as it takes into consideration the complexity and the multi-faceted structure of the research object. The first level of the model is reflected by models of separate target subspaces: territories of fishery; territories of extraction of mineral resources; territories for recreation. The ADL model is selected as a model of assessment for each target subspace. The model of the second level is presented in the form of a system of three equations, depending on the number of target indicators used for the evaluation of a socio-economic system influenced by subspaces. Each equation of the model used for the second level is also presented in the form of the ADL model. The article offers a new technique on how to determine the econometric equations coefficient.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".