Bibliographic record
Abstract
Community-based Ecotourism (CBET) holds great promise in promoting environmental conservation, local livelihood, and cultural preservation. However, without a clear understanding of the differential benefits and costs of CBET for men and women, ‘gender unaware’ CBET research, policies and projects may in fact promote the welfare of men over women. That is, without a consideration of factors such as (a) the gender division of labour, (b) gender relations, and (c) differential access to and control over environmental, livelihood and cultural resources, CBET projects may unwittingly exacerbate existing gender inequalities in local communities. As a result, CBET projects may fail to meet their fundamental environmental, livelihood and cultural aims. Following a brief discussion of CBET, this paper provides a rationale for gender analysis in ecotourism, outlines the historical evolution of gender and development work, and introduces standard frameworks and tools of gender analysis. Using extant empirical case studies of CBET, the paper then discusses how both ‘efficiency’ (Women in Development) and ‘empowerment’ (Gender and Development) tools of gender analysis might be applied in the research and practice of Community-based Ecotourism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".