Tourism in Mountainous Areas: Between Real Investment and Marginalization. A Case Study of the Central Ouarsenis Region, Tissemsilt State, Algeria
Bibliographic record
Abstract
Given the economic challenges that Algeria is aware of and its orientation towards finding an alternative economy for hydrocarbons, the tourism sector is experiencing a great recovery due to the various development programs enacted by the state in order to exploit Algeria’s tourist potential. In order to answer the central questions of this intervention, we used a number of sources such as books and studies related to tourism, as well as some statistical data from official Bodies, in addition to the field investigation of Sidi Suleiman and the regional protection areas of Ain Antar, to identify the regional and local radiation by knowing the volume of flows the sample size was 290, distributed as follows: 175 in Hammam Sidi Suleiman and 115 in the regional squad of Ain Antar. The tourism sector in Central Ouarsenis region has received calls for concerted efforts to develop and exploit it from various bodies and at all levels, due to diversity of Qualification in it , such as (monk homeland, landscape ... etc.), it is still waiting for the soul to advance the wheel of its development. The Central Ouarsenis region is considered a distinguished tourist attraction with high potentials for attracting tourists; however, this masterpiece of tourism is still suffering from some of marginalization due to the absence of the concerned authorities and the lack of sufficient awareness to develop the tourist sector in spite of available possibilities.
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".