New paradigms for the future: keynote perspectives from The R&D Management Conference 2008
Bibliographic record
Abstract
The R&D Management Conference 2008 theme of ‘emerging and new approaches to R&D management’ sought to draw out how R&D-based organizations today are changing the way they manage (in terms of novel approaches, techniques, models and tools) in face of the challenges and opportunities presented in the current environment. Six keynote presentations by executives, representing both the public and private sectors, elaborated on the following subjects reflecting their experiences on the theme: hyperconnectivity and changing R&D tenets, accelerating discoveries in human health via open access public-private partnerships, role of government in bridging the innovation gap, building sustainability and innovation in a traditional resource sector, R&D management in the aerospace sector, and leveraging diversity to build a culture of innovation. Their presentations highlighted amongst other things – global trends that are affecting how R&D organizations are operating, economic imperatives driving change in business models, working through partnerships within an open innovation environment, and leveraging the diversity presented by an increasingly globalized R&D workforce for success. Within these presentations are also challenges to researchers to generate new thinking to address current and future problems presented by the R&D environment. The keynote perspectives are summarized in this paper.
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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 | 0.021 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.038 | 0.031 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.024 | 0.028 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".