Exploring Tourist Narratives about the Animals in the Shanghai Zoo
Bibliographic record
Abstract
Based on ethnographic research conducted in the Shanghai Zoo during the first quarter of 2021, this report will explore how tourists' interpretations of the zoo animals are shaped by sociocultural contexts beyond the zoo border, which is, in turn, triggered by the zoo settings. The research involves 20 hours of participant observation, three short interviews with some visitors encountered in the zoo, and three more extended interviews with some friends who had visited the zoo with the author. This research confirms that tourists in the zoo interpret the zoo animals through broader sociocultural contexts beyond the zoo border based on their expectations and experiences of how animals are presented in the zoo. It is further argued that the tourists' desire to interpret the animals is related to the search for authenticity necessitated by encountering both the familiar and unfamiliar. The analysis of these interpretations focuses on reproducing meanings through one's familiar experience in scientific, cultural, and living domains. Built on existing research on the socioculturally inscribed meanings about animals, the search for authenticity in nature, and the projection of personal experience on animals, this report looks specifically at the role of zoo settings and the interplay of the "familiar," "unfamiliar," and "real" in the Chinese sociocultural context.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".