Be like a panda: reconstructing national identities through China's iconic species
Bibliographic record
Abstract
Despite lacking clear historical significance, the appeal pandas have to the people of China has played an integral role in the emergence of the country's cultural identity and ideals. Few studies have explored the giant panda due to the ongoing dialogue between the West and China, which, according to Edward Said, is permeated with imperialist, colonial, and orientalist flavors. In 1869, the French missionary Armand David encountered a dead specimen of a giant panda in Baoxing, Sichuan, which sparked the beginning of this dialogue. David shipped the skin to Paris, where the animal was named and aesthetically recreated for the first time for Western audiences. In this paper, we approach the giant panda as a dark tourism attraction embodying a process of making and remaking Chinese national identities over the past two centuries. Using “virtual curating” to study the Giant Panda Museum located at the Chengdu Research Base of Giant Panda Breeding, we demonstrate that the giant panda, which has achieved iconic status in China, represents a national history that is dark, backward, and based on suffering and death. We argue that understanding the giant panda's history as a dark tourism attraction provides an ethical vantage point from which to perceive tourist-panda relationships.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".