Geo-Spatial Data Visualization and Critical Metrics Predictions for\n Canadian Elections
Bibliographic record
Abstract
Open data published by various organizations is intended to make the data\navailable to the public. All over the world, numerous organizations maintain a\nconsiderable number of open databases containing a lot of facts and numbers.\nHowever, most of them do not offer a concise and insightful data interpretation\nor visualization tool, which can help users to process all of the information\nin a consistently comparable way. Canadian Federal and Provincial Elections is\nan example of these databases. This information exists in numerous websites, as\nseparate tables so that the user needs to traverse through a tree structure of\nscattered information on the site, and the user is left with the comparison,\nwithout providing proper tools, data-interpretation or visualizations. In this\npaper, we provide technical details of addressing this problem, by using the\nCanadian Elections data (since 1867) as a specific case study as it has\nnumerous technical challenges. We hope that the methodology used here can help\nin developing similar tools to achieve some of the goals of publicly available\ndatasets. The developed tool contains data visualization, trend analysis, and\nprediction components. The visualization enables the users to interact with the\ndata through various techniques, including Geospatial visualization. To\nreproduce the results, we have open-sourced the tool.\n
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| 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 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".