Make Way for the Algorithms: Symbolic Actions and Change in a Regime of Knowing
Bibliographic record
Abstract
When actors deem technological change undesirable, they may act symbolically by pretending to comply while avoiding real change. In our study of the introduction of an algorithmic technology in a sales organization, we found that such symbolic conformity led unintendedly to the full implementation of the suggested technological change. To explain this surprising outcome, we advance a regime-of-knowing lens that helps to analyze deep challenges happening under the surface during the process of technology introduction. A regime of knowing guides what is worth knowing, what actions matter to acquire this knowledge, and who has the authority to make decisions around those issues. We found that both the technologists who introduced the algorithmic technology, and the incumbent workers whose work was affected by the change, used symbolic actions to either defend the established regime of knowing or to advocate a radical change. Although the incumbent workers enacted symbolic conformity by pretending to comply with suggested changes, the technologists performed symbolic advocacy by presenting a positive side of the technological change. Ironically, because the symbolic conformity enabled and was reinforced by symbolic advocacy, reinforcing cycles of symbolic actions yielded a radical change in the sales' regime of knowing: from one focused on a deep understanding of customers via personal contact and strong relationships, to one based on model predictions from the processing of large datasets. We discuss the theoretical implications of these findings for the introduction of technology at work and for knowing in the workplace.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.055 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".