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Record W2179551177 · doi:10.5539/jsd.v8n9p121

Trailing and Projecting the Real Population of Bangkok to 2030

2015· article· en· W2179551177 on OpenAlexvenueno aff
Chanon Suwanmontri, Hiroyuki Kawashima

Bibliographic record

VenueJournal of Sustainable Development · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsFertilityTotal fertility ratePopulationDemographyPopulation projectionCapital cityGeographySocioeconomicsProjections of population growthBirth rateAge structurePopulation growthEconomicsFamily planningSociologyResearch methodology

Abstract

fetched live from OpenAlex

Due to imbalanced supplies of medical and healthcare resources between the capital city and other areas, Bangkok attracts its non-residents to access hospitals in the city only for giving births. This causes overestimation of Bangkok’s fertility rate and affects results of population projection. Therefore, this paper aims to project real Bangkok population numbers by age group and sex to 2030 by eliminating the influence of Bangkok-born outsiders. It introduces a new fertility rate calculation based on data from National Statistical Office of Thailand. The results show that in 2010 the total fertility rate of Bangkok was merely 0.8. All components being fixed, the projection displays shifts in population age structure, the age group with highest numbers from 25-34 years old in 2010 to 40-44 years old in 2030. Percent aging population expands from 9.6 to 22.6 percent. Furthermore, proportion of population aged 0-14 shrinks from 12.8 percent to 9.6 percent, which means Bangkok in 2030 is expected to face a seriously low number of young populations in opposite to its large number of elderly.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.034
GPT teacher head0.316
Teacher spread0.282 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2015
Admission routes1
Has abstractyes

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