FRONT-END OF INNOVATION METRICS: RESEARCH QUESTION AND LITERATURE REVIEW
Bibliographic record
Abstract
Em muitas empresas, reduzir os custos de fabricação para otimizar os lucros é uma estratégia comum usada para competir no mercado, sempre procurando reduzir os custos de fabricação e aumentar os lucros de ano para ano.No entanto, olhar para a otimização de custos não é mais eficaz à medida que novos concorrentes surgem no mercado que fornecem mais valor aos clientes. As empresas também devem competir impulsionando a inovação em produtos e serviços para se manterem competitivas no mercado. Para gerenciar e avaliar com eficácia o desempenho de um pipeline de inovação, ele deve ser medido, o que se torna difícil devido à falta de abordagens padronizadas.As seguintes três dimensões da FEI são investigadas neste artigo:• Modelos• Métricas• Linguagem comum
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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 | 0.023 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.029 | 0.033 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".