TRUSTED COMMONS: WHY “OLD” SOCIAL MEDIA MATTER
Bibliographic record
Abstract
Internet Studies scholarship tends to focus on new and hegemonic digital media, overlooking persistent uses of “older”, non-proprietary protocols and applications by some social groups who are key to configuring the nexus between technology and society. In response, we examine the contemporary political significance of using “old” social media through the empirical case of Internet Relay Chat (IRC) use. We advance a critique of platforms (closed, centralised, hegemonic social media) that we contrast with co-constructed devices that deeply involve users in their technological design and social construction. As a contemporary but long used online chat protocol, IRC serves as an important source for the critique of the currently hegemonic — but increasingly distrusted — infrastructures of computer-mediated communication. Drawing on Boltanski and Chiapello’s theory of critique and recuperation, we contrast the uses and underlying social norms of IRC with those of currently mainstream social media platforms. We claim that certain technical limitations that actors of IRC development did not feel necessary to address have kept it from incorporation into regimes of capital accumulation and social control, but also hindered its mass adoption. Ultimately, IRC continues to serve social groups key to the collaborative production of software, hardware and politics. While the general history of digital innovations illustrates the logic of critique and recuperation, our case study highlights the possibilities and pitfalls of resistance to it.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.089 |
| Scholarly communication | 0.025 | 0.037 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".