Innovation report 2014 : innovation, research and growth
Bibliographic record
Abstract
amongst the 100 most international universities globally.In order to further develop our strong international position, we are developing research and innovation collaboration frameworks with key powers around the world, and we are working with our world-class innovation institutions to encourage investment into the UK and enable UK businesses to export.We have worked with the European Commission to build the next innovation and research framework, Horizon 2020, and to improve access for UK businesses to EU funding programmes.The challenge now is to support our business community to access this funding opportunity effectively.This report provides us with an opportunity to reflect on the UK's successes and strengths.However, innovation, by its nature, never stays still.The time is right to reflect on what the UK's innovation system should look like in the next decade if we are to retain our position as one of the world's leading innovators and build upon the economic recovery.Therefore this document represents the start of a process culminating in a fresh long-term strategy for science and innovation to be released later this year.
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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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.059 | 0.074 |
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".