Premier référentiel de connaissances associées aux capacités organisationnelles de l'administration électronique
Bibliographic record
Abstract
In this article, we present an initial knowledge repository associated with the organizational capabilities of E-government. First, we define the concepts of organizational capability and knowledge repository. Second, we explain the procedures that researchers followed to develop this initial knowledge repository, that is the research objectives, the research question, and the methodology followed. Third, we present in detail the knowledge repository associated with the organizational capabilities of E-government. This knowledge repository is made up of an E-government evolution model, a conceptual modelling of the associated organizational capabilities, disciplines, and knowledge domains, a maturity model to assess organizational capabilities, and a management model for the development of the knowledge repository and of organizational capabilities. Finally, we discuss the limitations of this initial knowledge repository, especially the issue of placing it in context, which will enrich the codified knowledge from feedback linked to the implementation of E-government projects. In a next stage, the researchers will test empirically this initial knowledge repository to verify its operational validity. [PUBLICATION ABSTRACT]
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 | 0.012 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".