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Record W4411715159 · doi:10.32920/29416613

Demography - The Life and Death Discipline

2025· preprint· en· W4411715159 on OpenAlexaboutno aff
Philip Coppack

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsDemographyGeographyHistorySociology

Abstract

fetched live from OpenAlex

Human populations are a powerful engine of change and have had such an impact on the planet’s environments that it has been proposed (unsuccessfully) that we should have a new geologic epoch named after us: the Anthropocene. In this book I will explore the discipline of demography, or the study of human populations and especially how we measure various aspects of them. This will involve questions about where we came from, where we are currently, how we got here, where we’re going, and what we do and have done along the way. The book explores the discipline of demography, or the study of past, present, and future human populations. It expands on the complexities of the seemingly simple expression of births, deaths, migration and the resulting population change that is called the population model. It describes, compares, and analyses change, and explores forecasts within and between the world's human populations. It uses data from, principally, the United Nations Population Division, but also from the Wittgenstein Centre, and the Institute for Health Metrics and Evaluation. The possible impacts of predicted population changes on Canadian and global society are covered, as are questions about resource use and sustainable population size. The objectives of the book are (1) to explore demographic terms, tools, concepts, data sources, and language, (2) to develop practical skills to find, manage, manipulate, and analyze demographic data, (3) to gain a theoretical and applied understanding the causes and effects of demographic change in Canada, the world, and selected regions and nations, (4) to understand the importance of human populations in changing the planet, (5) to explore the impacts of human population on resource use and modelling, and (6) to explore what a “sustainable population” means, whether it is achievable, and what it would take to make it so. The book covers five principal areas of interest. First, a population dynamics section describes topics such as human capital, the population model, demographic change, fertility, migration, and forecasting. Second, a population structures section explores how demographic dynamics shape demographic structures such as demographic transition, population pyramids, survivorship, and dependency. Third, a population longevity and health section looks at dependency and health costs, aging, and health metrics. Fourth, a resources and sustainability section examines the impacts of demographics and population geography on politics and resources using the ‘two overpopulations’ concept premised on the ideas of Malthus and the Neo-Malthusians, contrasted with the Cornucopian perspective. The section also looks at the effects of the rapidly changing political economy of the world and the place of demography in shaping it. Fifth, a section on the growth and change in urban populations in included that looks at concepts of urban growth, urbanization and the differences between them that results in a three part transitions model of the urbanization process. Finally, the book ends with a discussion of what is meant by sustainable population and whether it is achievable. Each chapter explores this content in a data rich, analysis intensive, and narrative discussion of implications, ramifications, impacts, and some sense of what the rest of the 21st century may bring. This book is, in large part, just that: a formally referenced narrative on the quite literal life-and-death discipline that is demography. While grounded in evidence and argument—with more than 500 footnoted references and 360 figures, many of them data visualizations—it adopts a style somewhat looser than the standard textbook. Readers will find verifiable and evidence based conclusions interspersed with stylistic flourishes and, at times, sentiments that are candid and unvarnished. But throughout, it remains anchored to the principle that has always guided my work: ex testimoniis veritas—truth comes from evidence.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.006
Scholarly communication0.0040.006
Open science0.0010.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0080.002

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 head0.033
GPT teacher head0.341
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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