Through the Smokescreen of the Dieselgate Disclosure: Neutralizing the Impacts of a Major Sustainability Scandal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article analyzes the main neutralization techniques used in car manufacturers’ sustainability reports to disclose on the Dieselgate scandal. We conduct a conventional qualitative content analysis of 72 sustainability reports, covering the period 2013-2017, from 15 car manufacturers that were accused of unethical behaviors related to the measurement of diesel vehicle pollutant emissions. We then present a framework based on four configurations of neutralization techniques, namely, “head in the sand,” “self-proclaimed green leadership,” “wait and see,” and “start of a new era.” We describe that the manufacturers used heterogeneous neutralization techniques. Furthermore, the sustainability reports analyzed are relatively opaque and disconnected from the accusations made against the companies, which are widely reported by external sources. This article contributes to the emerging literature on the defensive impression management practices used to rationalize corporate misconduct in this area.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it