Novel Technologies and the Disruption of Markets, Organizations, and Fields
Bibliographic record
Abstract
This symposium showcases four papers that draw on a diverse array of theoretical perspectives to examine the rise of disruptive technologies such as cell-cultivated meat, non-fungible tokens (NFTs), artificial intelligence, and digital platforms. Collectively, the presentations in this symposium shed light on a variety of phenomena—including questions of how technologies that disrupt existing markets become legitimated over time; how technological advancements redefine the relationships between individuals, organizations, and the idea of what constitutes expertise; and how striving for ‘better futures’ drives the emergence of novel technological ideas and practices. The symposium integrates both macro- and micro-level perspectives to better understand the implications of novel technologies for organizations, markets, and fields. Taken together, the symposium aims to bring together scholars from various backgrounds to engage in an interdisciplinary dialogue on the pervasive implications of technological disruption for organizing. From NFT Hype to Legitimation: An Institutional Perspective Author: Timothy Hannigan; Telfer School of Management, U. of Ottawa Author: Michael Lounsbury; U. of Alberta Author: Rodrigo Valadao; NEOMA Business School Morality and Technological Evolution in the Emergent Field for Cell-Cultivated Meat Author: Magdalena Winkler; WU Vienna U. of Economics and Business Author: Elona Marku; U. of Cagliari Author: Giuseppe Delmestri; WU Vienna U. of Economics and Business Author: Maria Chiara Di Guardo; U. of Cagliari Monsters of Our Own Creation: AI, Occupational Cannibalization, and the Future of Work Author: Kevin Woojin Lee; U. of British Columbia The Public Evaluation of Physicians by Laypersons and Consequences for Professional Autonomy Author: Siddhant Ritwick; Doctoral Researcher Author: Johanna K. Moisander; Aalto U. Author: Kushagra Bhatnagar; Aalto U.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.076 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| 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".