Gender equality in a Chinese rural tourism destination: perspectives of females and males
Bibliographic record
Abstract
Gender equality is an important UN sustainable development goal. With tourism often encouraging and favoring female participation, relationships between tourism and gender equality have attracted much research attention. However, being dynamic and highly contextual, the multi-faceted complex of gender and tourism interactions are relevant to both males and females. Therefore, nuanced and contextual case studies incorporating perspectives of both genders is critically needed for gender research in tourism. Drawing from previous research, dimensions of gender equality are deconstructed into gender roles, perspectives, and relationships. Yudong Village, Zhejiang Province, China, was selected as the study site. Field investigations were used, primarily involving semi-structured interviews with multiple actors. Females and males were interviewed to acquire and compare their involvements in and perspectives on tourism and its influences on gender issues. Positive tourism influences on three dimensions of gender equality were acknowledged by both genders. Relationships among changes in gender perceptions, roles and relationships were examined. Agreements and differences between genders and across generations are discussed. Such changes rippled from family circles to rural communities through tourism enhanced social networks and business relationships. This research contributes to a gender balanced understanding of tourism impacts on gender equality. Practical implications are discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".