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Record W7128543663 · doi:10.64903/1480-6800-26.3-4.374

Tourism in Mountainous Areas: Between Real Investment and Marginalization. A Case Study of the Central Ouarsenis Region, Tissemsilt State, Algeria

2023· article· W7128543663 on OpenAlexvenueno aff
Sanaa Boualem, Bellal Sid Ahmed

Bibliographic record

VenueArab world geographer · 2023
Typearticle
Language
FieldSocial Sciences
TopicAfrican Studies and Geopolitics
Canadian institutionsnot available
Fundersnot available
KeywordsTourismExploitInvestment (military)Order (exchange)Diversity (politics)State (computer science)Sample (material)

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.005
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.284
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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