Hacking a reputation: crisis communication and the Ashley Madison data breach
Bibliographic record
Abstract
[Para. 1] At a time where online activists are targeting and obtaining the intellectual property of companies on a regular basis, how should a company communicate and mitigate the data breach to ensure that its valued customers feel protected, or in the best case scenario, prevent it altogether? The adoption and implementation of a sound crisis communication and management strategy is thus a fundamental operative for the success of any organization. Organizational crises can fundamentally disrupt and harm companies, organizations and individuals alike; they are characterized as “non-routine, severe event[s] that [can] destroy [its] reputation or operations” (Koerber, 2017). When a crisis arises for an organization, it is imperative that they have a strong sense of clarity regarding the issue at hand – specifically, they must understand the context and “background narrative that gives interpretative shape to [its] foreground issues” (Arnett, Deiuliis, Corr, 2017). Perhaps most emblematic of these background narratives is the circulation of competing information and perspectives, by both social media and traditional news sources. With the rise of social media and the 24/7 news cycle, a new sense of power and inflated ability to frame an issue has been afforded to many publics – particularly due to the ability of these mediums to rapidly transmit and receive information. These affordances have the potential to be either beneficial or detrimental to a company when faced with a crisis. While an organization can benefit from strategic media relations and effective crisis communication, even the most established of firms can have their voice become convoluted or be reprimanded if communication is poorly executed.
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 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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".