Alternative Imaginations: Confronting and Challenging the Persistent Centrism in Social Media-Society Research
Bibliographic record
Abstract
This article attempts to intervene the current trend in social media research that, to a certain degree, reflects the centrality of technology. Beyond the broad trend of technocentrism, I identify and outline four other major oversights or challenges in researching the social media/society relationship, namely online data centrism, moment centrism, novelty centrism, and success centrism. Stemmed from these four types of centrism, I offer an alternative imagination, namely a set of alternative pathways in social media research that value histories and historical context, interdisciplinarity, longue durée, and complexity. By revealing these oversights, this article aims to contribute to our collective attempt to interrogate the relationship between social media and society (and technology/society) critically. This alternative imagination might help animate, reveal, and make transparent various societal dynamics that otherwise would be invisible and, thus, might contribute to a better, deeper, and more comprehensive understanding of the technology/society relationship.
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.
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 | 0.044 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.010 | 0.111 |
| Scholarly communication | 0.030 | 0.044 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.007 | 0.016 |
| 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".