Who Gets to Fly?
Bibliographic record
Abstract
Abstract In this chapter, we posit that academics need to reduce their flying in line with the ‘Carbon Law’ if we are to attain the agreed-upon targets of the Paris agreement. This entails reducing emissions in general as well as reducing emissions from flying by at least 50 per cent every decade from 2020 and on. We present data from KTH Royal Institute of Technology regarding our flying and use two specific departments as examples. We unpack this data, using material visualisations (i.e. post-it notes and poker chips) to raise questions that are not immediately apparent when looking at top-down statistics about flying. Our material visualisations instead present data about flying patterns and habits in a format that viscerally displays the differences (‘inequalities’) that exist between and within departments. Such visualisations emphasise that reducing the frequency and the length of air trips will inevitably lead to discussions and negotiations about who gets to fly (or not), as well as discussions about exactly what constitutes ‘unnecessary’ flights. The chapter ends with a reflection about the limitations of our language and how the task of reducing carbon emission from flying necessitates a reinvention of how we think and talk about flying.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".