We Are All Theorists of Technology Now: A Relational Perspective on Emerging Technology and Organizing
Bibliographic record
Abstract
Technologies are changing at a rapid pace and in unpredictable ways. The scale of their impact is also far-reaching. Technologies such as artificial intelligence, data analytics, robotics, digital platforms, social media, blockchain, and 3-D printing affect many parts of the organization simultaneously, enabling new interdependencies within and between units and with actors that many organizations have typically considered to be outside their boundaries. Consequently, today’s emerging technologies have the potential to fundamentally shape all aspects of organizing. This article introduces the special issue “Emerging Technologies and Organizing.” We treat these new technologies as “emerging” because their uses and effects are still varied and have yet to stabilize around a recognizable set of patterns and because the technologies themselves are, by design, always changing and adapting. To theorize the relationship between emerging technologies and organizing, we draw on relational thinking in philosophy and sociology to develop a relational perspective on emerging technologies. Our goal in doing so is to create a new way for organizational scholars to incorporate the ever-increasing role of technology in their theorizing of key organizational processes and phenomena. By developing a relational perspective that treats emerging technologies not as stable entities, but as a set of evolving relations, we provide a novel way for organizational scholars to account for the role of technology in their topics of interest. We sketch the outlines of this relational perspective on emerging technologies and discuss the implications it has for what organizational scholars study and how we study it.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.066 |
| Scholarly communication | 0.018 | 0.035 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".