When Big Brother Is Benevolent: How Technology Developers Navigate Power Dynamics among Users to Elevate Worker Interests
Bibliographic record
Abstract
Existing research on technologies-in-use has overlooked how contemporary technology developers interact with users to shape the effects of workplace technologies, and what role power plays in these interactions. In this qualitative study of a digital manufacturing monitoring technology, I examine how third-party developers pursued ongoing acceptance from high-powered manufacturing managers while attempting to incorporate the interests of low-powered manufacturing workers across their client base. I develop a two-stage model that depicts the underlying sources of divergent user preferences and the practices that developers used to navigate these differences. First, in response to cross-occupational differences between managers and workers within client firms, developers used alignment moves to pursue interest alignment while encoding workers’ preferences into design prototypes. Next, when facing cross-firm differences due to pushback by managers in some contexts, developers used buffering moves to limit the influence of these managers and release the new features. As a complement to examining local variation in use, I suggest that future research on workplace technologies should focus on developers’ capacity to disrupt managerial control across different clients and user groups. Because they enact jurisdiction over ongoing design and development, developers are an important professional group to consider in contemporary technology ecosystems.
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 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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".