Developing Improved Tools for the Economic Analysis of Innovations in the Bioeconomy: Towards a Life Cycle-Strengths-Weaknesses-Opportunities-Threats (LC-SWOT) Concept?
Bibliographic record
Abstract
The bioeconomy is one of the areas with the greatest innovation potential and also the highest degree of complexity, in relation to both the articulation of technologies using biological resources and the range of human values involved. As a result, this area of the economy increasing calls for the early evaluation of new technologies both from a business and societal perspective. The objective of this paper is to review the literature on the existing instruments designed to provide an (economic) analysis of new technologies in the bioeconomy sectors (in particular the well -known concepts of Life Cycle Analysis - LCA and Strengths-Weaknesses-Opportunities-Threats -SWOT), and to devise avenues for the improvement of such instruments. Specifically, the paper focuses on developing the idea of a Life Cycle-Strengths-Weaknesses-Opportunities-Threats methodology (LC-SWOT) as a potential tool for improving the ability to evaluate early stage technologies in relation to the entire technology/product life cycle.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".