Sense-making Accountability: Netnographic Study of an Online Public Perspective
Bibliographic record
Abstract
Accountability has been proposed and interpreted in multiple ways by scholars. However, the understanding of accountability from a public perspective, especially in the form of an enterprise offering public services, is not well understood. This study tries to shed light on accountability through a political crisis: the cancellation of power plants by the ruling political party in Ontario, Canada just prior to a provincial election. This study aims to extend our understanding of the multidimensional nature of accountability by gathering insights from the online public community. A netnographic approach was conducted on five social media sites. Six articles from these sites disseminating the crisis were selected and a total of 1313 associated comments were subjected to content analysis. We found the facets of accountability as perceived by the public somewhat differently from the perceptions of organisations. Multidimensional facets of sense-making accountability are identified, including the assigned meanings from the public (holders), the role of stakeholders (holders and holdees), the nature of public responses (emotional and reasoned responses) to a crisis and possible consequences (recommended actions) as suggested from the public. The recommended actions will ultimately help with the process of fostering accountability, and encouraging a new cycle of accountability development.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".