The Atlas of Canada Web Mapping: The User Counts
Bibliographic record
Abstract
Imagine if…A student searches the Internet to get information for a project on Victoria for a grade nine geography class. He uses Google to search for “Victoria” and “geography”. First on the list of search results is The Atlas of Canada. He quickly selects this and arrives at the home page of the Atlas. He sees that he can search for a place and he does so for Victoria. He finds there are many places named Victoria in Canada and is able to find the one in British Columbia for which he is looking. He then sees that he can link from the location map to combine themes with the place. The thematic material includes all the types of information he is required to put into his project. Not only can he see the maps, which he decides to use as the basis for his project, but also the background data used to make the map. He notices an audio button that he clicks on to get a description of the map and a video button, which brings up an interesting video clip. He then finds that a full description is available that also provides links to other sites that may be of interest. Everything he needs is in this one great Web site. From now on, The Atlas of Canada is where he will start all his assignments.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.020 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.014 |
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".