Bibliographic record
Abstract
The evolution of the Information Age, in Canada, has meant an unheralded parallel social evolution—the development of a class structure, if you will, that is tied to data accessibility. While other countries have made data freely available for use by industry, education and the public, Canada has opted to follow a restrictive data policy under which data are essentially available to a select few—those who can afford the prices. While anyone can purchase the data, not everyone can pay the price. The implications of this in our society are immense and are felt throughout our social structures. One obvious example of this is the lack of quality, high‐resolution Canadian data freely available for use in the Canadian education system, particularly in the university classes in which students today are usually introduced to GIS, visualization and data interpretation. Our students have data to work with, but often they are the freely available American data. They learn from examples derived in the mountains of Wyoming or the forests of Washington. How did this Canadian data restriction happen? In this paper, the evolution of GIS classicism is explored through examination of the evolution in Canada of GIS itself. The data situation elsewhere in the world is reviewed, the feasibility of ‘freeing’ data is discussed and a call for a radical change in the way data/information are handled in Canada is presented.
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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".