In the Wake of Digitalization: Examining Processes and Tensions of Technological Change
Bibliographic record
Abstract
This symposium examines the myriad consequences wrought by digital transformations in organizations and industries: intended, unintended, and corollary. As categorized by Scott and Orlikowski (2022), intended consequences generated through technological change are usually visible and anticipated; unintended consequences are similarly visible and direct, but unanticipated; and less visible, indirect corollary effects occur when digitalization challenges institutional values, norms, and rules in industries, potentially displacing them. Papers in this symposium address research questions related to knowledge production, workers’ mobility in labor markets, and change management, all with respect to how digitalization transforms organizations and industries. By exploring these questions in research contexts such as Wikipedia, digital labor markets, information services, and agencies that maintain public infrastructure, this symposium advances research on how digitalization transforms industries in not only a direct, but also indirect, pathways. By grappling with different kinds of “changes occurring at some temporal and spatial remove from the main events” (Orlikowski & Scott, 2023, 2), the four papers in this symposium provide the opportunity to clarify and build on conceptual differences among different types of technological changes and their outcomes using examples of industries being digitalized. Anticipating the future of Wikipedia’s regime of knowing in the GenAI era Author: Shani Evenstein Sigalov; Tel Aviv U. Author: Lior Zalmanson; Coller School of Management, Tel Aviv U. Author: Stella Pachidi; U. of Cambridge Examining Workers’ Mobility in Online Labor Markets Author: Hatim A. Rahman; Northwestern Kellogg School of Management Anticipatory Control in the Digital Transformation of Water Infrastructure Author: Virginia Leavell; Cambridge Judge Business School Software as subterfuge for subordination: Unintended consequences of digitalization in a library Author: Jennifer Lauren Nelson; U. of Illinois at Urbana-Champaign
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".