Front-Line Professionals in the Wake of Digital Scrutiny: The Paradox of Public Accountability
Bibliographic record
Abstract
Digital technologies such as smartphones and social media are enabling increased monitoring, reporting, and online dissemination of issues that members of the public may have about organizations and its front-line professionals. How do front-line professionals respond to the public’s increased digital scrutiny, and with what consequences for organizational accountability? As organizations care about regaining the reputation lost due to such negative reporting, prior research predicts that the public’s use of such technologies to voice their concerns and demand more accountability should improve organizational accountability. However, findings from my 24-month ethnographic study of emergency management organizations (EMOs) suggest that the public’s increased digital scrutiny of organizations and its employees can, under some conditions, paradoxically end up worsening accountability. My study unpacks the processes that generate this paradox of public accountability – front-line professionals’ increased risk aversion, undermined role identities, strained role relations, and resource lock-up. Together, these processes reshape the work of front-line professionals and produce a vicious cycle of coordination that worsens organizational accountability. I synthesize these findings and develop a model of the paradox of public accountability. Through a matched case research design, I then compare two structurally similar EMOs facing the above challenges to highlight the importance of role-rotation in reducing the risk-aversion of front-line professionals, and thereby disrupting the vicious cycle of coordination. This research generates insights into the ways in which organizational accountability is being reconfigured in the digital age through the shifting work practices of front-line professionals responding to increased public scrutiny.
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| 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".