Implementing Electronic Services Transnational Guidelines and Perspectives
Bibliographic record
Abstract
Electronic services to citizens are a growing concern to governments all over the world, not in the least in the domains of social security and labor market. It was at the Montreal Conference of the ISSA â the International Social Security Association â in 1999 that many organizations in many countries showed to be grappling with many questions concerning the implementation of electronic service delivery. In order to elaborate on experiences of implementation, the ISSA and three Dutch member organizations arranged an expert work shop on implementation strategies for E-government in social security in the Autumn of 2000. This report summarizes the experts conclusions on strategies, methods, do s and don ts. It emphasizes the importance of a mix of technological, political, legislational and organizational prerequisites. The considerations encompass the following domains or perspectives: (i) Infrastructure, being the technical devices such as network components, servers, protocols, instruments for client identification, which needs some cooperation or coordination between social security organizations; (ii) Data management, which poses the question how governments can avoid to ask citizens or employers for the same information twice; (iii) Standards and responsibilities, dealing with scope, and with how they are to be established, implemented and maintained; (iv) Client appreciation, one of the key issues when designing the services, which ones and how; (v) Issues of flexibility, which are related to changes in legislation, in technical standards and client appreciation; and last but not least: (vi) Costs and benefits, the context of justification for investments. For each domain or theme, context, goals and experiences are stated first. Only a few examples are described in the report itself. Each theme ends with do s and don ts, aiming at the promotion of action, at the reduction not the ignorance of complexity. A range of illustrative cases is described in a separate appendix.
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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 | 0.049 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.034 | 0.023 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".