Bibliographic record
Abstract
This article argues that China's agreement in 2012 to loan Canada two panda bears is emblematic of animals’ simultaneous material‐symbolic inclusion and exclusion in contemporary politics. Employing a material focus, this article draws a connection between the panda gift and the promise to which it is attached: a promise of material flows; of Chinese access to Canadian resources, especially; and controversially, tar sands oil. Common to geographical flows of both pandas and oil is a devaluation of nonhuman life. In one flow, two pandas cross the Pacific for a decade of captivity at the Toronto and Calgary Zoos, where visitors will pay to view the permanently visible pandas. In the other instance, oil will be shipped across the same ocean, oil whose production comes at great cost to wildlife, including caribou, birds, and fish, and whose spill at any point along its journey to China would devastate marine and terrestrial wildlife populations. Stark power imbalances between species are at the heart of both of these flows and their material consequences. This article argues that, in emphasizing what the pandas symbolize, the extent to which their own and others’ lives are materially affected is elided.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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".