Contested artifact: technology sensemaking, actor networks, and the shaping of the Web browser
Bibliographic record
Abstract
Much of IT research focuses on issues of adoption and adaptation of established technology artifacts by users and organizations and has neglected issues of how new technologies come into existence and evolve. To fill this gap, this paper depicts a complex picture of technology evolution to illustrate the development of Web browser technology. Building on actor‐network theory as a basis for studying complex technology evolution processes, it explores the emergence of the browser using content analysis techniques on archival data from 1993‐1998. Identifies three processes of inscribing, translating, and framing that clarify how actors acted and reacted to each other and to the emergent technological definition of the browser. This spiral development pattern incorporates complex interplay between base beliefs about what a browser is, artifacts that are the instantiation of those beliefs, evaluation routines that compare the evolving artifact to collective expectations, and strategic moves that attempt to skew the development process to someone's advantage. This approach clarifies the complex interdependence of disparate elements that over time produced the Web browser as it is known today.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".