Successful, small scale, manufacturing from five European island regions
Bibliographic record
Abstract
The support of the European Commission, through its Leonardo da Vinci Community Vocational Training Action Programme, in this ongoing pilot project- MT/2002/B/F/139000- is gratefully acknowledged. This manual has been compiled by University of Malta Professor Godfrey Baldacchino (the NISSOS Project ideator and academic coordinator) with the kind support of the project’s 11 institutional partners, their delegates and associated support staff involved in the NISSOS Project. These are: Estonia Chamber of Commerce and Industry-Saaremaa Branch (Delegate: Tullio Liblik); Kuressaare College at Tallinn Technical University (Maret Pank); Åland Trade Association (Mika Lindfors & Johnny Mattson); Åland Polytechnic (Anna-Lena Freman, Christer Kullman & Thor-Bjorn Wik); Technological Institute of Iceland (Bjorn Gislason); Institute of Business Studies at the University of Iceland (Gylfi Dalmann Adalsteinsson); Foundation for Human Resources Development, Malta (Helga Ellul, Mario Grixti, Godwin Micallef, John Muscat Drago); Malta Enterprise (Karl Herrera); University of Malta (Roger Ellul Micallef, Saviour Rizzo); Malta College for Arts, Science and Technology (Frank Edwards); the Highland Council of Scotland (Catriona Maclean; Lisa Stephen) and the UHI Millennium Institute, Scotland (Maggie Marr; Anne Marie McDairmid, Stephanie Tristam). Thanks also to the expert independent input of Joe Vella Bonnici (Malta), Ingi Runar Edvardsson (Iceland) and Tage Petersen (Denmark). The institutional support of the University of Prince Edward Island, Canada, is also acknowledged. Responsibility for the contents of this manual and any errors are those of the author and are not attributable to the European Commission or any of the specific NISSOS Project institutional partners.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".