Translating climate change: Anthropology and the travelling idea of climate change
Bibliographic record
Abstract
In the prairies of Alberta, Canada, winters are cold, wood is scarce. This place is home to Native Americans – many of them are highly educated nowadays. One summer, a young Native American Chief, college-educated and incapable of reading the signs of Mother Nature, was asked by his people how cold the next winter will be. Embarrassed of not mastering the traditional skills for predicting the weather, and to be on the safe side, he said to his people: ‘Well, I think this will be a pretty cold winter this year.’ He then sought help from his college friend, a meteorologist at the local Weather Channel station. ‘Tell me, Joshua, don’t you think we are facing a cold winter this year?’ Equally unable to predict the weather so far ahead, and also to be on the safe side, Joshua the meteorologist confirmed the Chief’s opinion: ‘Oh, I think this will be a really cold winter’, was his answer. So the Chief went back to his people and announced: ‘Folks, this year, I know, the winter will be particularly cold – let’s all join forces to collect as much wood as we can’. A few weeks later the Chief asked Joshua for a more accurate prediction of the winter. The meteorologist answered: ‘I am certain this will be an extremely cold winter!’ Back with his people, the Chief announced: ‘People – I have signs that this winter will be so cold that none of our ancestors, as long as our memory reaches, have encountered. Let’s collect all the wood we can find!’ Just before the winter, the Chief consulted his meteorologist friend again, and the meteorologist told him: ‘This is going to be a record-breaking winter!’ Curious about his certainty, the Chief asked: ‘Joshua, tell me, how can you be so certain about this?’ To which the meteorologist replied: ‘You know, my friend, I have never seen this before in my entire life: all the Native Americans have been collecting wood like crazy this year’ (adopted from Huang 2013, 415 – 416, in de Wit 2017, 151).
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".