Revolutions: an introduction to the #AoIR2023 special issue
Bibliographic record
Abstract
This paper introduces the ‘Revolutions’ themed special issue which includes research presented at the 24th annual Association of Internet Researchers (AoIR) conference (2023). The conference theme centered on revolutions, highlighting the connections between digital transformations and social movements across time and space. Focusing on the affordances of digital technologies for mobilization, resistance and achieving social justice, but also their limitations in enabling lasting social change, the conference theme asked participants to reflect on the tradeoffs between empowerment and subordination, and the relationship of digital ‘revolutions’ to racial justice, anticolonial movements, and the rising tide of white supremacist and fascist mobilization. This special issue includes six papers that offer new angles on critically assessing the groundbreaking early ideas underpinning online networked spaces and questioning the revolutionary potential of the internet today. The range of papers includes contexts related to platform power and user agency, online political subcultures and memeification, the balance between visibility and power for content creators revolutionizing live streaming and influencer cultural industries, and perceptions of AI’s revolutionary impact on romantic relationships. The studies in this issue also offer a global view, with geographies stretching from the MENA region and China to subcultures and marginalized groups in Western contexts such as the US and Canada.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.072 | 0.029 |
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".