How Covid-19 Has Changed Safety in the Car Transportation Sector: A Corpus-Assisted Analysis of Non-Financial Reports
Bibliographic record
Abstract
The importance of people’s safety during Covid-19 has resulted in greater attention to this aspect in CSR and ESG disclosures, especially in the field of transport. The relevance of this topic is also reflected in the recent publication of linguistic studies that investigate how companies in some sub-sectors communicate their commitment to passengers’ safety (Rossato & Nocella, 2022; Bondi & Nocella, 2023). This paper explores how safety is discursively constructed and communicated in the car rental and ride sharing sectors, both before and during the pandemic. Working along the lines of corpus-assisted discourse studies (Partington et al., 2013), the research analyses a corpus of English CSR and ESG reports published by international American companies in the above-mentioned fields of transport. Findings suggest a great concern for safety issues and a more inclusive approach to both customers’ and employees’ safety during the pandemic. Additionally, car rental companies show more attention to vehicle safety, while ride sharing operators put more emphasis on road safety. Evidence also suggests that the companies largely employ commissive statements and vague linguistic choices that signal a general lack of transparency on the practices and initiatives enacted to ensure customers’ and workers’ safety.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".