Introducing intellectual potential – the case of Alfa Laval
Bibliographic record
Abstract
Intellectual capital has gained increasing attention concerning both research and more practically oriented applications during the past five years. Intellectual Capital and other knowledge management tools are topics that have emerged in the light of a broader trend of redirecting the foundation of competitive advantage from the company's tangible assets to its intangibles such as knowledge base, brands and the content and structure of computer‐based systems. In this paper, the concept of intellectual potential is introduced. Intellectual potential is a further development of intellectual capital, using four principles: strategy basis; management orientation; process orientation; and context sensitivity. The concept is a tool for the strategic management of an organisation's intangible assets in order to increase its long‐term revenue‐generating capabilities. The case of Alfa Laval is used as an illustration of how intellectual potential can add value as a management tool.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".