Grassroots versus Established Actors’ Framing of a Crisis: Tweeting the Oil Spill
Bibliographic record
Abstract
Recent crises as well as the rise of social media have made it especially theoretically and topically important to understand how grassroots actors, i.e. actors who are not institutionally recognized and established, frame a crisis. This research contrasts and relates grassroots actors’ to established actors’ framing (e.g. media and social movement organizations). Building upon framing and social movement literatures as well as upon emerging works on social media affordances, we develop mixed-methods analyses of framing related to the Gulf of Mexico oil spill of 2010 and produced through the popular microblogging platform Twitter. Building upon empirical findings, this research conceptualizes why and how, with microblogging, grassroots actors’ framing of a crisis does not simply diffuse but rather builds upon, reacts to and opposes established actors’ framing. It adds to the literature by showing and conceptualizing how, through microblogging, grassroots actors do not only come together with others who share similar frames, but also provoke others who hold opposite frames. It finally reveals that grassroots framing through microblogging may contribute to exacerbate the crisis.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".