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Record W4387971761 · doi:10.36922/ijps.474

Human development, population, and environmental burden: Historical perspective and a peek into the future

2023· article· en· W4387971761 on OpenAlexaff
Niels C. Lind

Bibliographic record

VenueInternational Journal of Population Studies · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGross domestic productIndustrialisationPopulationHuman Development IndexWorld populationEconomic growthChinaGeographyConsumption (sociology)Human development (humanity)Development economicsSocioeconomic statusIndex (typography)Human Development ReportProduct (mathematics)Socioeconomic developmentSocioeconomicsEnvironmental protectionEconomicsDeveloping countryDemographySocial scienceSociology

Abstract

fetched live from OpenAlex

The human species has continuously progressed in health, wealth, education, and population worldwide since industrialization. A measure of this advance, the Development Progress Index (DPI), is applied here to the world from 1770 to the present and then projected to the year 2100 for three shared socioeconomic pathways. Concurrently, our total environmental impact continues to grow with population and consumption. However, progress has been uneven across regions. While China is projected to outdistance the United States, India is projected to surpass both this century. The population keeps growing, and the average individual DPI-value has now grown enormously - by a factor of 17 since 1770. The environmental burden to sustain the human lifestyle is reflected by the world’s gross domestic product that has meanwhile grown by a factor of 155. If such human progress is to continue apace, the gross world product will be more than 2000 times higher by 2100. Already now a concern, the environmental impact is projected to grow five times larger by 2100. Human environmental impact needs a measure and attention.

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)0.000
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 score0.029
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.017
GPT teacher head0.296
Teacher spread0.279 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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