Bibliographic record
Abstract
The Arab Spring, Occupy Wall Street, and the Spanish Indignados movements have garnered a great deal of attention among those seeking to understand the role of social media in protest activities. Whereas some have argued that social media enhance freedoms and can lead to transformative changes (e.g., Castells, 2012; Earl & Kimport, 2011; Shirky, 2008), others have noted the limitations of commercial platforms and the potential for slacktivism rather than activism within those platforms (Dean, 2005; Fuchs, 2014 Gladwell, 2010; Hoofd 2012; Morozov, 2009; Poell & Van Dijck, 2016). Zizi Papacharissi’s recent work Affective Publics: Sentiment, Technology, and Politics contributes to this discussion, but takes it into an exciting new domain. Rather than debating for or against the role of social media as a space for political effect, she is interested in how people use these online spaces for political affect. In other words, she wants to understand how social media provide new ways for people to express themselves and participate in what she terms the “soft structures of feeling” (p. 116) that help people feel that their views matter and are worthy of expression in this particular moment. First, she argues, we feel like we are a part of the developing story, and then, as we contribute our own emotive declarations online through our words, photos, and videos on Twitter, Facebook, or in other social media venues, we become a part of the story.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".