Bibliographic record
Abstract
This paper explores the role of gift exchange in tourism, focusing on how it appears in Japanese travel guidebooks, especially the Globe-Trotter Guidebook (Chikyu ˉ no Arukikata).While travel has long been tied to the spread of monetary economies, practices such as souvenirs, hospitality, and charitable giving show that non-monetary exchanges still persist.The study first conceptualizes two directions of gift exchange: from travelers to local people (souvenirs, donations, volunteer activities, or treating someone to a meal) and from local communities to travelers (hospitality, invitations, or small gifts).It then analyzes how the Globe-Trotter Guidebook represents these practices.Early editions included "souvenir" in packing checklists, suggesting small Japanese items to give to hosts, but such references gradually disappeared, except in special cases such as Mongolia or Canada.Hospitality was occasionally depicted, notably in Turkey, but over time shifted from celebratory accounts to warnings about scams or harassment.Three trends emerge: the decline and disappearance of gift-exchange references, their treatment as optional rather than necessary, and their simplified cultural interpretation.These trends suggest a shift toward market-based exchange as the dominant framework in guidebooks, reducing the visibility of reciprocal social relations between travelers and local communities. 『地球の歩き方』に見るオミヤゲと歓待： ツーリズムにおける贈与交換のための試論
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".