Interactive Map to Illustrate Seat Distributions of Political Party Support Levels: A Web GIS Application
Bibliographic record
Abstract
Canada's new electoral order of 338 districts, updated in October 2013, was in effect for the October 2015 federal election. A Web GIS application was developed using the new electoral map to display seat projections generated by the Laurier Institute for the Study of Public Opinion and Policy (LISPOP). Seat projections convert, or “project,” public opinion support for the major political parties into a distribution of electoral seats for each party, based on a detailed analysis of an aggregation of polls. The map was published through ArcGIS Server, and coding language HTML and JavaScript implemented the required functions. Two key interactive features of the map include: (1) pop-up windows that provide the 2011 election result and LISPOP's current seat projection when hovering over a constituency; and (2) a drop-down menu that direct users to a desired region. The use of ArcGIS software enables fast and effective updates before and during the election.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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".