Exploring the Use and Adoption of Workplace Automation through Metaphors: A Discourse Dynamics Analysis
Bibliographic record
Abstract
Organizational metaphors represent an important study area in the information systems (IS) field. In this paper, I review previous work on organizational metaphors in IS research and build on this work by proposing a discourse dynamics approach to metaphors as an alternative lens for conceptualizing and studying IS metaphors. With this approach, one can recast organizational metaphors from something that researchers commonly perceive as detached from the subjects they investigate—a view fixed in much IS thinking—to something that results from both language and the mind, that has a situational nature, and that individuals can deploy in flexible and dynamic ways. Drawing on in-depth focus group studies, I illustrate the discourse dynamics approach via analyzing metaphors that individuals made in describing workplace automation. With this study, I not only raise new questions in relation to theorizing about and analyzing organizational metaphors in IS research but also illustrate metaphors’ usefulness as a form of sense making to generate fresh insights into the implications that arise from adopting and using workplace automation that remain unnoticed if one used more conventional methods.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".