Bibliographic record
Abstract
This thesis builds on contemporary observations of a qualitative change in the way research and development (R&D), invention and innovation activities take place: an increasingly open, distributed or network-like character of innovation activities with the locus of innovation shifting from an individual entrepreneur to hybrids, consortia or networks (Bouba-Olga & Grossetti, 2007; Laursen & Salter, 2006; Chesbrough, 2006, 2003; Powell & Grodal, 2005; Gassmann & Enkel, 2005; Coombs et al., 2001; Powell & Brantley, 1992); an increasing geographic reach of innovation activities , and a geographic topography best characterised as ‘local nodes in global networks’ (Belussi et al., 2010; Cooke, 2008; Moodysson, 2008; Coenen, 2006; Asheim & Gertler, 2005; Bathelt et al., 2004). Increasingly, innovation activities take place on different geographic scales, combining the best of local resources and expertise with global ones. Moreover, geographic scope has broadened from the traditional research countries (North America, Canada, Europe and Japan) to newcomers, particularly Russia, India, China and, to a lesser extent, Brazil (the BRICs) (Boekholt et al., 2009; Howells, 2008; UNCTAD, 2005). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.614 | 0.402 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".