* * Centre de recherche en géomatique
Bibliographic record
Abstract
Programme de DESS en Geomarketing For a few years now, several development projects have been carried out in municipal contexts in order to make spatial information available on the Internet. An overall observation of the existing municipal Web sites obviously shows the great variety of the objectives at stake, and of the technological solutions implemented. Despite the increasing number of researches dealing with the democratization of e-information addressed to citizens and e-governments, it is still difficult to clearly identify the current privileged means of communication between cities and citizens on the basis of cartographic data. This difficulty is related to the absence of formal and effective frameworks to characterize and classify the various ways to diffuse geospatial information on municipal Web sites. On the basis of the above, the present research aims at remedying this ignorance by elaborating a new classification framework to effectively describe GIS-based Web sites in municipal contexts. The adopted strategy consists in analysing the already existing partial approaches of classification, in order to pursue with the development of a more comprehensive pragmatic classification framework. This framework is then the subject of an experimentation consisting of a detailed analysis of the contents and functioning of a hundred existing municipal Web sites in Canada. Finally, this experimentation makes it possible to draw initial conclusions regarding the usability of the new classification mode proposed, as well as to identify some further research pathways.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.224 | 0.101 |
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".