Reframing algorithmic public opinion: Affect, recursive dynamics, and vernacular practices
Bibliographic record
Abstract
This commentary builds on Alessandro Gandini, Silvia Keeling, and Urbano Reviglio's concept of algorithmic public opinion by positioning it in dialogue with my decade-long research on social media algorithms and politics in Southeast Asia, notably the notion of algorithmic enclaves. I offer three key interventions. First, I foreground affective textures as central to algorithmic circulation, emphasising the centrality of affect in the algorithmic marketing culture . Second, I propose a recursive model of public opinion, where opinions are continually re-formed through iterative feedback loops, blurring linear distinctions between process and product and foregrounding the cyclical entanglement between direct and indirect gatekeeping . Third, I introduce the idea of vernacular visibility modulation , revealing how ordinary users, through culturally embedded and affectively attuned practices, shape what becomes visible in algorithmic environments, while not necessarily acting strategically. Together, these interventions extend the current framework by attending to affective texture, the recursive dynamics, and the ambient forms of agency. Reversing the gaze, the commentary argues that insights from the non-Western context may offer critical comprehension into how algorithmically mediated public opinion is formed, felt and contested globally.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".