Proximity ethics, climate change and the flyer’s dilemma: Ethical negotiations of the hypermobile traveller
Bibliographic record
Abstract
This article offers a reading of proximity ethics as a novel way of understanding the moral dilemmas that underpin decisions of whether or not to fly. The question of why people fly, despite holding pro-environmental attitudes and knowing that their behaviour, in contradiction, is harming the earth they value, is not an easy one to answer. Through a co-constructed narrative method, we examine our own flying activity in relation to the proximal ethical decisions in the intersection of family, social and work domains. Our stories highlight that the tensions between normative positions on climate change and travel activities are bound up in the ethical proximal relations that compel intimate contact with others, create the need for face-to-face contact and impel obligation in family/work/social domains in a globalised world. Proximity ethics illuminates the flyer’s dilemma as a complex and tenuous web of moral decisions, in which care and proximity play key roles in guiding actions. The contribution of this article lies in its exploration of the quandaries of human behaviour associated with climate change mitigation, using moral philosophy as a window of understanding onto our increasingly technological and hypermobile world.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.044 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".