Unlocking the potentials of community ecotourism: a promising agent of post war reintegration and sustainable development for Ethiopia and Eritrea
Bibliographic record
Abstract
The article was presented in a launching seminar of ‘Centre for Peace and Reconciliation in Mekelle University, focused on ‘Restoring Ethio-Eritrean Relations from Bottom-up: People-to-People Reconciliation and its Contribution to Social Cohesion, Economic and Infrastructure Development’. The paper attempts to explore potentials and roles of community ecotourism in improving the livelihood of border communities and influences post-war re-integration of the society in both countries. The design of the research was qualitative approach. Data sources for this research were aligned to primary and secondary aspects. The primary sources were gathered through interviews with individuals, office holders and experts in the industry and visitors from both countries, and documents. Observations were experienced in the tourist destination sites in Tigrai, Ethiopia. Secondary sources were assessed to address the literature-based discussions. Therefore, findings revealed, the availability of high potentials for community ecotourism engagements in both countries. There are encouraging transport accesses, religious pilgrimages. The availability of variety foods, suitability of ecotourism eco-lodges, seashore in Eritrea; and the UNESCO registered sites and carnivals. There are excellent practices for sustainable ecotourism development linked to the environmental protection and land management. During the observations, Ethio-Eritreans appeared harmoniously enjoying the religious and non-religious carnivals. Therefore, sustainable ecotourism contributes to reconciliation, healing and peace building. The author recommends the participation of all stakeholders in the sustainable development of the community-based ecotourism. In this regard, the sector needs comprehensive socio-economic baseline survey to support the development initiatives.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".