Bibliographic record
Abstract
Corinne: The more you think about climate change, the more you start thinking that you need to change everything in your life. Even when I’m on my smartphone, I can't help thinking: How much energy was used to make this? How much fuel was consumed to mine the metals, or transport the components from China, California, Malaysia, Taiwan? How much energy is required to heat the Apple store? How much electricity is used to charge the battery? It is hardly surprising that many climate activists conclude that everything in our behaviour, habits and lifestyles needs to change. This also makes the challenge feel insurmountable. At the other extreme, the Canadian climate economist, Mark Jaccard, has written that the focus on the need for individual lifestyle change is a myth. Eric: Individual lifestyle decisions are often a central element of “solutions” in climate campaigns. This approach often reads more like a wish-list than a focused strategy for social change. The real challenge is delivering a huge global investment drive to make electricity sustainable and electrify all energy use. The implicit view of psychology is also naive. Changing behaviour is not as simple as responding to ethical arguments and lists in books. And yet, as we mentioned in Chapter 1, there are some important areas where we may not be able to rely on either investment or new technology, and we do need to change behaviour. In which case we need a realistic understanding of social change and behavioural psychology. Corinne: The typical to-do list says “eat less meat”, “car share” and “consume less”. On the fuzzy end of the spectrum, we’re advised to practice mindfulness, plant trees, and work to find our purpose. There's something fundamentally unhelpful about much of this, which is worth being blunt about. Even the engaged, informed and the motivated don't change their behaviours like this. I have a guilty history of susceptibility to fast fashion. I have to fight the thrill of filling a digital basket with cheap fashion goodies, the big bag arriving, and new outfits galore. You get a serotonin hit from it. Marketing strategies play on the precise psychological traits that Dan Ariely describes. I’ve managed to quit fast fashion now, but I still struggle to wean myself off the odd burger. The recommendations in books like How To Avoid A Climate Disaster , or The Future We Choose , are spot on.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.857 | 0.004 |
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".