Bibliographic record
Abstract
Effective innovation management in knowledge- and technology-intensive industries such as the Life Sciences critically depends on the appreciation of organizational intangibles as key factors in innovation processes. Furthermore, successful innovation processes in these industries critically depend on cooperation across actors and disciplines. Collectively referred to as the organization’s intellectual capital (IC), intangibles entail the non-physical, non-monetary means that are available to the organization through its employees, organizational structures, and external relationships. Management of IC is complicated due to the complex, elusive, and context-specific nature of organizational intangibles, making it difficult to identify, appropriate, and effectively utilize valuable intangibles in cooperative innovation processes. Building on previous studies into innovation barriers, drivers, and appropriability challenges in PharmaNutrition and the Life Sciences more broadly, this thesis focuses on the dynamics of IC within these industries, aiming to advance the utilization of intangibles for cooperative innovation. A sequential research strategy was implemented, combining a multiple desk study design with a multiple case study design to first synthesize applicable conceptual frameworks and models for innovation and organizational IC (desk studies), and then apply these concepts to different Life Sciences contexts (case studies). A total of nine individual studies were conducted and together answered the central research question: How can we advance the management of organizational intangibles for cooperative innovation in the Life Sciences?
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".