Bibliographic record
Abstract
A number of environmental political theorists have called for representation of animals, ecosystem or the earth in general in policy making. But political representation of nonhumans raises some difficulties for liberal political theory. No less an authority than Hannah Pitkin gives us reason to think nonhumans cannot be given political representation. Pitkin insists that political representation cannot take place unless the represented can be (conceived as) capable of independent action and judgment, not merely being taken care of. I argue that this objection is overstated. Drawing on the work of Andrew Rehfeld, Michael Saward and Jennifer Rubenstein, I argue that representation is best conceived as a way to create political agency for nonhumans. I explore how nonhuman animals are in fact represented in American politics, how their representatives are authorized and held accountable, and how we evaluate their representative claims. I conclude, however, that there are limits to the representation of nonhuman interests, and some of Whiteside's concerns about relying on the representation model are valid.
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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 | 0.024 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.089 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".