On Cooling the Tourist Out Notes on the Management of Spoiled Expectations
Bibliographic record
Abstract
This article focuses on the social world of the commercial whale watch cruise. It draws on several years of participant observation research with marine field scientists, particularly field scientists who serve as naturalists on commercial whale watch cruises. Using Erving Goffman’s work, the essay details how the naturalist’s narration is an example of “cooling the mark out” that Goffman conceptually outlined and others have explored. In the social world of the commercial whale watch, the naturalist is the “operator and the tourist the mark”. It is argued that the naturalist’s narration is the principal means for cooling the tourists’ out. This is done within a context of the operator anticipating a set of spoiled expectations the tourist is likely to experience. While this essay extends the work of Goffman and others who have explored different settings of the cooling out process, it substantially differs from them. Past studies have focused on the cooling out process primarily within a context of individual face-to-face interaction. This essay looks at the commercial whale watch as a social setting of cooling out the mark not on a face-to-face basis but as a process of a “group of individuals who are being “cooled”. Most importantly, this is viewed as occurring not after they have been conned or duped but in anticipation of their likely experiencing a set of spoiled expectations.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".