Innovation, Identities and Resistance: The Social Construction of An Innovation Network
Bibliographic record
Abstract
This paper explores the process of diffusion of a socio‐technical innovation among workers of a large microelectronics firm. Actor–network theory (ANT), which draws on the sociology of science and technology, is applied to the analysis of socio‐technical innovation in order to understand the actions of creating and putting the actors’ arguments into action. Actors constructed and organized these arguments with the aim of diffusing innovation among workers whose support was essential to the project’s success. The authors of the innovation project wanted to change the state of relations between different actors. In the present study, the aligment of identities was established according to the criteria defined by the managers and engineers but the expected benefits of the innovation, in this case, technology and teamwork, were not automatically accepted. Network analysis reveals how persuasive arguments that repudiate the old reality and justify steps to create the new reality are constructed. This article will reveal how innovation is constituted and the form it takes by following the chain of arguments and the responses of the actors involved.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".