The adoption of corporate social responsibility practices in the airline industry
Bibliographic record
Abstract
This paper identifies initiatives related to corporate social responsibility (CSR) in the airline industry and evaluates the overall state of their adoption as reported by members of the three largest airline alliances. Of 41 airlines, only 14 had annual CSR reports publically available in January 2009. Reports were analyzed using a qualitative content analysis approach. Results showed a stronger focus on environmental issues than on the social or economic dimensions of CSR. Of the seven major environmental themes examined, emission reduction programs predominate. Other environmental issues receive much less attention, with no single other initiative implemented by all airlines. Four social and environmental themes were found, including employee wellbeing and engagement, diversity and social equity, community wellbeing and economic prosperity. The data analysis supported the arguments made in the literature that the airlines report CSR initiatives using differing or inconsistent measurements, making evaluation and comparison of their performance and effectiveness difficult. Although a large number of airlines publishing CSR reports discussed their achievement of major goals (reduction of emissions, increasing community involvement or increasing workforce diversity), a much smaller number provided detailed information relating to specific initiatives implemented in order to contribute to these goals. Further, important issues for CSR research are posed in the paper.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".