Bibliographic record
Abstract
Purpose Reviews the latest management developments across the globe and pinpoints practical implications from cutting‐edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings A total of 34 visionary companies have been chosen as Technology Pioneers 2009 by the World Economic Forum. Technology Pioneers are companies from around the world that develop and apply the most innovative and transformational technologies in the fields of information technology, renewable energy and biotechnology/health. The range of companies awarded the title is extremely diverse and includes; Canadian based BioMedica, which has the primary mission of dissemination of affordable Medical Diagnostics in the developing world, NovaTorque which has invented highly efficient electric motors that achieve half to a quarter of the losses of conventional motors and 50‐100 percent higher power densities, US based mPedigree which manages the first system anywhere in the world by means of which consumers and patients can instantly verify the source of a purchased pharmaceutical at no cost, at the point of purchase, using standard mobile phones, and Qifang Inc., which is based in China, and has developed an innovative peer‐to‐peer lending platform that is now helping to bring together students and those who can help fund their education. Practical implications Provides strategic insights and practical thinking that have influenced some of the world's leading organizations. Originality/value The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy‐to‐digest format.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.125 | 0.049 |
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".