Material-discursive practices in technology standards development: A topic-modeling approach to technology evolution
Bibliographic record
Abstract
Social interactions play a vital role in shaping technology evolution, especially in technology standards development involving multiple actors across the industry. However, previous studies mostly focus on the interactions between technology designs and socio-cognitive factors and pay little attention to the intertwined nature of social and material aspects of technologies. From the sociomaterial perspective, a key is to focus on the process of discursive materialization and its performative consequences in practice. Drawing on Orlikowski and Scott’s (2015) material-discursive perspective, this paper examines how the HTML5 (technology standard) evolves over time by investigating the processes of discursive materialization at the World Wide Web Consortium with the topic modeling techniques. The analysis shows that four fundamental mechanisms (process management, dialogical coordination, boundary work, and knowledge conversion) shape the evolution of HTML5. This study contributes to understanding processes of technology evolution from the sociomaterial perspective with a novel empirical approach.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 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".