Minds and Machines: Expertise in an Age of Intelligent Machines
Bibliographic record
Abstract
Intelligent machines are transforming the nature of knowledge, skills, and expertise, challenging many of our assumptions about work and organizing. Researchers have long emphasized the impact of emerging technologies on reshaping interactions within organizations and occupational communities. From paper mill operators with software systems (Zuboff, 1988), radiologists with computerized CT scanners (Barley, 1986), librarians with internet search (Nelson & Irwin, 2014), and NASA scientists with open-source innovation (Lifshitz- Assaf, 2018) scholars have found that the introduction of digital technologies can occasion changes to occupational identities and trouble the boundaries of domain knowledge within and between organizations. However, our understanding of expertise in the era of machine learning, algorithms, and AI is still nascent. Unlike previous digital technologies, intelligent machine applications can handle complex decision-making tasks and analysis of large amounts of structured and unstructured data, disintermediating the tasks of managers and workers (Kellogg et al., 2020; Murray et al., 2021; Faraj et al., 2018). As such, recent calls for research emphasize the need for more theorizing on expertise and more empirical studies on how workers, occupational communities, and organizations can adapt to and cultivate the skills needed in this new world of work (Heimstädt et al., 2023; Nicolini et al., 2022). Therefore, this symposium provides new perspectives and insights at the nexus of intelligent machines and the evolving nature of knowledge, skills, and expertise. It will consist of two conceptual and three empirical papers that grapple with differing forms of intelligent technologies and their impacts. In concert, these presentations foreground and question the assumptions and heuristics that scholars of work, management, and organizing have traditionally held preceding the proliferation of intelligent machines. This symposium is designed to encourage discussion and integrate diverse theoretical and methodological approaches to the evolving landscape of work and technology. How Autographic Affiliations Shape Patterns Of Technology Use Author: Callen Anthony; New York U. Ethical Expertise in the Era of Fair Algorithms in Organizations Author: Sarah Lebovitz; U. of Virginia Author: Emmanouil Gkeredakis; IESE Business School Monsters of Our Own Creation: AI, Occupational Cannibalization, and the Future of Work Author: Kevin Woojin Lee; U. of British Columbia Integrated Organizational Training in the Age of Artificial Intelligence Author: Hatim A. Rahman; Northwestern Kellogg School of Management Characteristics as a Complement to Process: Theorizing Skill in an Age of Intelligent Machines Author: Matt Beane; U. of California, Santa Barbara
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.039 |
| Scholarly communication | 0.015 | 0.030 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".