MétaCan
Menu
Back to cohort

Demography: Historical

2020· other· en· W4320163187 on OpenAlexaff
Carl Mosk

Bibliographic record

VenueThe Blackwell Encyclopedia of Sociology · 2020
Typeother
Languageen
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDemographic transitionHistorical demographyIndustrial RevolutionPopulationPopulation growthIndustrialisationAgricultural revolutionExtant taxonDomesticationDemographic historyDemographic changeFertilityGeographyHistorySecular variationDemographyAgricultureSociologyPolitical scienceEcologyBiologyArchaeologyDeveloped countryEvolutionary biologyLaw

Abstract

fetched live from OpenAlex

Abstract Demographic history is shaped by technological breakthroughs, such as the Neolithic Revolution beginning around 10,000 bce , and the first Industrial Revolution in the eighteenth century. Hunting and gathering peoples experienced anemic population growth; the diffusion of settled agriculture exploiting domesticated plants and animals engendered slow secular increase – albeit punctuated by dramatic reverses – in human numbers. The first and second Industrial Revolutions unleashed dramatic growth. Given the importance of understanding population dynamics, theories have flourished: notable are Malthusian theory exploring preindustrial demography, and demographic transition theory accounting for demographic developments after industrialization. Given the extant statistical record, research in demographic history implicitly differentiates between core and periphery zones. In the core, documentation being relatively satisfactory, evidence points to homeostatic Malthusian equilibriums ultimately ushering in demographic transitions, fertility often increasing prior to experiencing secular decline. In the periphery where evidence is scanty, theorists of population dynamics have struggled to apply principles applicable to the core.

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), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.110
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.274
Teacher spread0.257 · 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 designNot applicable
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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueThe Blackwell Encyclopedia of SociologySame topicInsurance, Mortality, Demography, Risk ManagementFrench-language works237,207