FRONT-END OF INNOVATION METRICS: RESEARCH QUESTION AND LITERATURE REVIEW
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it