Crowd-Based Accountability: Examining How Social Media Commentary Reconfigures Organizational Accountability
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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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".