Through the Smokescreen of the Dieselgate Disclosure: Neutralizing the Impacts of a Major Sustainability Scandal
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.
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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 | 0.019 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".