From Disruptive Technology to Disruptive Strategy: Europlasma a Case Study of Sustain Entrepreneurship
Bibliographic record
Abstract
Sustainable entrepreneurship is underpinned by the action of entrepreneurs attempting to detect exploit environmentally-orientated social market opportunities capable of generating value creation through future innovations. In greening industries, these newcomers are like Davids trying to propose disruptive strategy and find some room for themselves alongside existing Goliaths and injecting a bit of green into their strategy. The question is what kinds of emergence models they might wish to adopt. The present texts responds with two complementary analyses - one formulated in terms of “dominant design”; and another that applies an “evolutionist” vision where small and large companies learn from one another. Comparing several propositions in a framework shaped by a case study of the firm Europlasma - an SME operating in the waste treatment sector - it highlights the role played by contingent variables and suggests partial conclusions rooted in the emergence modalities available to eco-industry start-up
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".