Bibliographic record
Abstract
Presented at the 2nd Annual Digital in Government Conference - Ottawa (CA) - June 2021 Planning and organising a digital transformation within the EU, at governmental level, is challenging for a wide range or reasons: some are specific to the EU whereas the others are inherent traits of any digital transformation initiative. After a brief characterisation of the EU environment, and more specifically the Council of the European Union, the speaker will share his experiences on three major recent digital transformation initiatives where he is (or has been) directly involved, as programme manager, architect or digital transformation officer: •The ‘Council decision making Programme’ (CODEMAP), aiming at enhancing decision making within the Council of the EU; •The ‘Information and Knowledge management’ (IKM-) Programme, aiming at improving the user experience of the EU Member States delegates and the staff of the General Secretariat of the Council, while exchanging information and sharing knowledge on EU policy matters. •The “e-Justice” initiative aiming at promoting the digitalisation of the justice within the EU, along with the ‘e-CODEX’ programme that is the backbone network connecting the EU Member States and EU judiciary players and supporting the exchange of data on civil or criminal matters. The speaker will also briefly evoke the EU initiatives (‘Connecting Europe Facility’, ‘Digital Europe’, and the ‘Recovery and Resilience Facility’ recently adopted as a recovery plan for the EU) along with the EU laws (e.g. the General Data Protection Regulation, the Council Security regulation, the Open data directive or the ‘Digital agenda for Europe’) insofar as all these plans and pieces of legislation have (or will have) a huge impact on the digital transformations initiated at governmental level within the EU.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.063 | 0.007 |
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".