Talking about Technology: The Emergence of a New Actor Category Through New Media1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it