How to Improve the Social Utility Value of Geographic Information Systems for French Local Governments? A Delphi Study
Bibliographic record
Abstract
Today, geographic information technologies (GITs) stand out as the unavoidable answers to the French local governments' new stakes. Yet, an important discrepancy has been noticed between the utility levels (in the qualitative sense) and the theoretical intrinsic potential of these technologies. The social utility value of GIT seems quite low compared with the quantitative level at which they are diffused. The authors focus on the ‘determination of value’, by considering the obstacles to the development of a spatial data infrastructure in the French context. From the results of a Delphi study, the authors bring to the fore the fact that the institutional and organisational barriers 0ack of a clear policy in matters of access and dissemination; cost of public data; absence of fully operational norms and standards; failure to raise the awareness of the potential users as a whole; etc) more than technical difficulties, are the prime causes of this phenomenon. Through this analysis, the authors emphasise the need to organise a French national spatial data infrastructure, strongly linked with most of the local initiatives developed by the local governments.
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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 | 0.087 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".