An Analysis of COSPA – A Consortium for Open Source in the \nPublic Administration
Bibliographic record
Abstract
This paper reflects on a two-year EU funded \nspecific research targeted project that officially began \nin January 2004 entitled COSPA, a Consortium for \nstudying, evaluating and supporting the introduction of \nOpen Source Software and Open Data Standards in the \nPublic Administration. COSPA focuses on office \nautomation and desktop system software and aims at \nrigorously measuring the effort, costs and benefits of a \ntransition to Open Source. The project involves 15 \nEuropean Universities and Public Administrations \nfrom Italy, Hungary, Ireland, Denmark, UK, Belgium \nand about sixty observers, including the University of \nAlberto (Canada), Victoria University of Wellington \n(New Zealand) and the United Nations Educational \nScientific and Cultural Organisation (UNESCO).
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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 | 0.030 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.024 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".