We Are All Theorists of Technology Now: A Relational Perspective on Emerging Technology and Organizing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it