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Record W7098221316

Appalachian Migration Patterns, 1975-1980 and 1985-1990

2016· article· en· W7098221316 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioactive Natural Diterpenoids Research
Canadian institutionsnot available
Fundersnot available
KeywordsAppalachiaAppalachian RegionMetropolitan areaPopulationQuarter (Canadian coin)PovertyImmigration
DOInot available

Abstract

fetched live from OpenAlex

following characteristics:! Appalachia experienced remarkable demographic change; in- and outmigration accounted for a turnover of over a quarter of the region’s population.! Appalachia has become an amenity region for students seeking higher education; over 100,000 more college-enrolled students came into the region than departed it.! Older Appalachians have been aging in place; relatively few persons 65 or older were newcomers to the region.! Appalachia has become more diverse; African-American and Hispanic populations increased through migration, especially in the Southern sub-region.! Appalachia has become poorer; migrants entering the region had lower-status jobs, lower incomes, less education, and were more likely to be living in poverty than those leaving the region.! Conditions vary widely among Appalachian sub-regions. Northern and Central Appalachia have been losing population while simultaneously becoming a destination for low-income, blue-collar migrants with little formal education. Southern Appalachia has been gaining population, and its inmigrants were more ethnically and racially diverse, better paid, more educated, and worked at higher status jobs than did migrants entering the other two sub-regions.! Appalachian migration patterns have changed from long-range flows into distant metropolitan areas to short-range exchanges principally centered around cities in and immediately adjacent to the region.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.513
Threshold uncertainty score0.981

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.264
Teacher spread0.255 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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