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Record W7008978719

Coordinated Population Forecast for Malheur County, its Urban Growth Boundaries (UGB), and Area Outside UGBs 2016-2066

2016· article· en· W7008978719 on OpenAlexaboutno aff

Bibliographic record

VenuePDXScholar (Portland State University) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationPopulation growthPopulation declineNatural population growthPopulation projectionNet migration rateProjections of population growth
DOInot available

Abstract

fetched live from OpenAlex

Different parts of the County experience different growth patterns. Local trends within UGBs and the area outside them collectively influence population growth rates for the County as a whole. UGBs in Malheur County include Adrian, Jordan Valley, Nyssa, Ontario, and Vale. Malheur County’s total population declined slightly in the 2000s; however, some of its sub-areas experienced minor population growth during this period. The population growth that did occur in Malheur County in the 2000s was largely the result of natural increase (more deaths than births). An aging population not only led to an increase in deaths but also resulted in a smaller proportion of women in their childbearing years. This, along with more women having fewer children and having them at older ages has led to births stagnating in recent years. A larger number of births relative to deaths caused a natural increase) in every year from 2001 to 2017, though natural increase waned throughout this period, resulting in minimal population change. Total population in Malheur County as a whole, as well as within its sub-areas, will likely decrease at a similar pace in the near-term (2019 to 2044) compared to the long-term. Population decline is a product of net out-migration outpacing natural increase. Malheur County’s total population is forecast to decline by 655 people over the next 25 years (2019-2044) and by roughly 1,620 people over the entire 50-year period (2019-2069).

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score0.999

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 studies0.0020.000
Scholarly communication0.0000.001
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 head0.023
GPT teacher head0.229
Teacher spread0.207 · 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
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
Published2016
Admission routes1
Has abstractyes

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