GeoConnections framework data guide
Bibliographic record
Abstract
Welcome to the GeoConnections Framework Data Guide. This online course is designed to introduce you to framework data concepts, sources and uses. Does your job require you to present information in a geospatial or map form? Do you need to bring together geospatial information from different sources and integrate it on a common base map? Have you had difficulties finding and using common base mapping data? If the answer to any of these questions is yes, this guide is for you. Framework data is common base map data that provides geospatial reference across Canada to physical features and other types of information that is linked to geography. You can access it from a number of sources at a variety of scales or levels of information detail. Framework data is important because it provides you with a foundation for integrating other kinds of data, which is often required for analysis and reporting purposes. The guide has two purposes: To inform you of the benefits of framework data. To help you find and access framework data. If you have a general background in information technology or use it to analyse data, prepare charts and reports, etc., but are not familiar with geospatial information and its manipulation, this guide will help you. Once you have read the guide, you will know where to find framework data, understand the benefits of different data sources, be in a better position to choose the type of data that best suits your needs, and know how to access it from your own computer.
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 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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.285 | 0.199 |
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".