La prestation de services publics par message texte : les types de services et les facteurs d’acceptation
Bibliographic record
Abstract
La plupart des services cybergouvernementaux dans les pays en développement n’ont pas réussi à mobiliser la population. L’écart entre ce qu’exige la technologie d’Internet et le piètre progrès des technologies de l’information dans ces pays serait la principale barrière à l’utilisation de ces services, mais la prestation de services publics par message texte (SMS) pourrait pallier cet écart. Notre article explique pourquoi le recours à ce canal constitue une bonne stratégie pour joindre les citoyens et les sensibiliser à l’utilisation des services publics électroniques, notamment dans les pays en développement. Nous énumérons les services gouvernementaux offerts par SMS à l’aide d’un modèle à six niveaux et décrivons également treize facteurs cognitifs et affectifs qui influencent le citoyen dans sa décision d’accepter ou non les services. Nous terminons en proposant des recommandations aux gouvernements pour faciliter une adoption rapide des services gouvernementaux par message texte.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".