Crowd-Based Accountability: Examining How Social Media Commentary Reconfigures Organizational Accountability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Organizational accountability is considered critical to organizations’ sustained performance and survival. Prior research examines the structural and rhetorical responses that organizations use to manage accountability pressures from different constituents. With the emergence of social media, accountability pressures shift from the relatively clear and well-specified demands of identifiable stakeholders to the unclear and unspecified concerns of a pseudonymous crowd. This is further exacerbated by the public visibility of social media, materializing as a stream of online commentary for a distributed audience. In such conditions, the established structural and rhetorical responses of organizations become less effective for addressing accountability pressures. We conducted a multisite comparative study to examine how organizations in two service sectors (emergency response and hospitality) respond to accountability pressures manifesting as social media commentary on two platforms (Twitter and TripAdvisor). We find organizations responding online to social media commentary while also enacting changes to their practices that recalibrate risk, redeploy resources, and redefine service. These changes produce a diffractive reactivity that reconfigures the meanings, activities, relations, and outcomes of service work as well as the boundaries of organizational accountability. We synthesize these findings in a model of crowd-based accountability and discuss the contributions of this study to research on accountability and organizing in the social media era.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it