Mapping Production Activity in Yukon: Experimental Estimates of Grid Square-Based Gross Domestic Product
Bibliographic record
Abstract
In recognition that more geographically granular economic data improves our ability to understand the nature of production, support regional economies, and address emerging socio-economic and environmental problems, statistical agencies are increasingly asked to produce gross domestic product (GDP) estimates at finer levels of geography. This demand is being met in different ways around the world, with, for instance, the European Union producing GDP estimates at the Nomenclature of Territorial Units for Statistics level and the United States producing GDP estimates at the county level. While Canada produces GDP estimates for census metropolitan areas, it does not currently produce the same level of coverage for smaller geographies as does the European Union or the United States. This paper addresses this gap by developing subprovincial and subterritorial grid square-based GDP using the Yukon as a test case. The Yukon was chosen because its small resource- and government-based economy provides a challenging but comprehendible test of these fine-grained measures. This choice will also support ongoing work measuring the economies of circumpolar regions. With this in mind, the paper has three objectives. First, it introduces and discusses the benefits a fixed grid for measurement. Second, it identifies the types of data necessary to estimate GDP across a 1 km2 grid. Lastly, it produces a set of grid-based GDP estimates that serve to describe the geography of economic output in Yukon.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".