Bibliographic record
Abstract
On September 18, 2015, Volkswagen became embroiled in a global crisis after the U.S. Environmental Protection Agency (EPA) publically announced Volkswagen’s violation of the Clean Air Act through the installation of “defeat devices” that trick emission tests. The Volkswagen emissions scandal was covered by media around the world and news spread quickly on social media networks, such as Twitter, though a trending hashtag, #dieselgate. Through studying Volkswagen’s Twitter accounts (the Twitter account for the overall brand, a regional Twitter account and a Twitter account targeting the press), this case study analyzes Volkswagen’s adoption of the Situational Crisis Communication Theory’s (SCCT) rebuild and bolstering crisis response strategy on Twitter, but with little open communication through this medium. Information shared on Volkswagen’s Twitter accounts was inconsistent and Volkswagen’s limited adoption of a conversational, human voice on social media affected the virality of organizational messaging. Volkswagen was progressing towards recovering its social currency on Twitter, but updates on the crisis or similar news related to the situation encourages greater hostility and apathy towards the organization.
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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".