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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.144 | 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; both teacher heads agree on what is shown here.
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".