Shaping the Future: How Novel Fields and Market Categories Evolve
Bibliographic record
Abstract
This symposium delves into the complexities of emerging technological fields and market categories, and how they gain wider adoption despite their novelty and associated challenges. The symposium will include discussions on the various stages of their evolution, the dynamics of their legitimacy, their impact on existing structures and industries, and the relationships among various stakeholders. Scholars will scrutinize the theoretical aspects and practical implications of these emerging fields, providing invaluable insights for navigating these evolving landscapes. The discussions aim to provide insights into the complexities underpinning the birth, development, and growth of innovative fields and categories, acknowledging their role as fertile ground for theorizing. Vision-reality gap: The Co-evolution of Visions and Technologies in Robotics (1921-2020) Author: Mia Chang-Zunino; ESCP Business School Building Bridges with Ambiguity: Category Innovation in Deep Tech Author: Angelo Romasanta; ESADE Business School Author: Jonathan D. Wareham; ESADE Legitimating the Non-comparable: Rise of Plant-based Meat and Transmutation of the Meat Category Author: Jade Lo; Drexel U. From Symbolic to Consequential: Role Enactment and Brokering in a New Market Category Author: Brandon H. Lee; Melbourne Business School
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.047 |
| Scholarly communication | 0.028 | 0.035 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".