Bibliographic record
Abstract
There are many things we can choose to do about climate change, including doing nothing at all. All of them have consequences, many of which will be unforeseen. If we could foretell more accurately what would happen to the climate in the future, our choices might be clearer, if not necessarily easier to make. Unfortunately, predicting future climate change is fraught with uncertainty, and we will be forced to make choices in the face of that uncertainty. To what extent are we motivated in this difficult process by a desire to do the “right thing”? And how do we decide what is the right thing to do? The answer to these questions depends on whose ethical interests are considered. What is best for a Canadian living in the last decade of the twentieth century—even supposing we could discover what that is—might not be best for a Somali, or for our great-grandchildren, or for the rain forest of the Amazon or the kangaroos of Australia. Decisions about what to do about global warming will therefore be influenced by how much relative weight we give to the ethical interests of Canadians, Somalis, grandchildren, rain forests, kangaroos and a host of other variables. Weighing these competing interests is an exercise in applied ethics. This book examines the role that ethics can and should play in our decisions about how to deal with global warming.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".