The Diffusion of GIS: A French Canadian Cross-Cultural Comparison of the Impact of National GI Policies
Bibliographic record
Abstract
Public policies orientations, and especially those dealing with Information Technologies and land planning policies, have very strong impacts on national diffusion of Geographical Information Technologies and associated spatial databases. Actually, this field seems to be a very powerful way for the GIS community to improve the understanding of the relationship between GI and society (see for example the last GISOC Conference that took place in Minneapolis in June 1999). Besides, a few studies have already more or less directly explored this research topic. Recently, Lopez (1998) compared precisely how USA and European scientific and technical information (STI) policy impacts the dissemination of Geospatial databases. He shows how government information policy directly influences the availability and commercialization of public Geographical Information. But this work does not explicitly take into account the strong connections between, on the one hand, the national cultural context, the institutional organization of the national territory and the priorities of planning policies, and, on the other hand, the diffusion process and social adoption of Geographical Information at the different scales of planning activities. Nevertheless, Information Technologies and Geographic Information policies are embedded into larger
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".