Talking about Technology: The Emergence of a New Actor Category Through New Media1
Bibliographic record
Abstract
This paper examines how a new actor category may emerge in a field of discourse through the new media of the Internet. Existing literatures on professional and organizational identity have shown the importance of identity claims and of the tensions surrounding “optimal distinctiveness” for new actors in a field, but have not examined the roles of new media in these processes. The literature on information technology (IT) and identity has highlighted the identity-challenging and identity-enhancing aspects of new IT use for existing actor categories but has not examined the dynamics associated with the emergence of new actor categories. Here, we investigate how a new actor category may emerge through the use of new media as a dynamic interaction of discursive practices, identity claims, and new media use. Drawing on findings from a case study of technology bloggers, we identified discursive practices through which a group of technology bloggers enacted claims of a distinctive identity in the joint construction of their discourse and in response to continuous developments in new media. Emergence of this new category was characterized by ongoing, opposing yet coexisting tendencies toward coalescence, fragmentation, and dispersion. Socio-technical dynamics underlying bloggers’ use of new media and the actions of prominent (“A-list”) bloggers contributed to these tendencies. We untangle theoretically the identity-enabling and identity-unsettling effects of new media and conceptualize the emergence of a new actor category through new media as an ongoing process in which the category identity may remain fluid, rather than progress to an endpoint.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.015 | 0.042 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".