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Record W6949231158 · doi:10.5281/zenodo.1163817

Tourism-Led Poverty Alleviation In South Asia – An Analytical Rapportage

2018· article· en· W6949231158 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2018
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyTourismContext (archaeology)South asiaExtreme povertyPanacea (medicine)Quarter (Canadian coin)Population

Abstract

fetched live from OpenAlex

Presently, when the whole world is busy celebrating the technological advancements, accumulated wealth and gains from globalization along with industrialization, poverty continues to stand as a ubiquitous and prevalent predicament in South Asia which is home to a quarter of the world population and nearly one third of the poor people in the world. Albeit, there has been a size-able<br> reduction in the poverty headcount ratio since 1990, about 256 million people remain in absolute poverty in this region. Inequality both in social (e.g. gender, social strata and standard of living) and economical (e.g. per head income, Gini index and wealth distribution) terms is adding fuel to fire. However, the region has shown tremendous progress in tourism thanks to its natural, man-made and cultural endowments. In this paper, the authors have argued that tourism has predominant predicaments to be the panacea for poverty in South Asia. Tourism-led poverty alleviation studies have mostly been carried out in South East Asian context and this paper can bridge this gap. Various<br> initiatives, policies and measures aimed at poverty alleviation through tourism have been highlighted<br> in this paper.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesScholarly communication, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0060.014
Open science0.0040.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.004

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 head0.082
GPT teacher head0.313
Teacher spread0.232 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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