Social Accountability, Ethics, and the Occupy Wall Street Protests
Bibliographic record
Abstract
Abstract This study examines the 3.5 m+ English-language original tweets that occurred during the 2011 Occupy Wall Street protests. Starting from previous research, we analyze how character terms such as “the banker,” “politician,” “the teaparty,” “GOP,” and “the corporation,” as well as concept terms such as “ethics,” “fairness,” “morals,” “justice,” and “democracy” were used by individual participants to respond to the Occupy Wall Street events. These character and concept terms not only allowed individuals to take an ethical stance but also accumulated into a citizen’s narrative about social accountability. The analysis illustrates how the centrality of the different concepts and characters in the conversation changed over time as well as how the concepts ethics, morals, fairness, justice, and democracy participated within the conversation, helping to amplify the ethical attributes of different characters. These findings contribute to our understanding of how demands for social accountability are articulated and change over time.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".