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Record W609680709 · doi:10.34051/p/2020.213

Immigration to Manchester, New Hampshire

2014· report· en· W609680709 on OpenAlexaboutno aff
Sally Ward, Justin Young, Curt Grimm

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationRefugeePovertyLatin AmericansDiversity (politics)Political scienceUrbanizationImmigration policyEconomic growthGeographyDevelopment economicsDemographic economicsHistoryEconomic historyLawEconomics

Abstract

fetched live from OpenAlex

This brief analyzes immigration and refugee resettlement in Manchester and the effects on the city’s demographic composition, as well as the implications for its future. Authors Sally Ward, Justin Young, and Curt Grimm report that Manchester, New Hampshire, like the nation, is experiencing a new wave of immigration. In the past, most of the city’s immigrants tended to come from Canada and Europe. Today, they are most likely from Latin America, followed by Asia and, to a lesser extent, Africa. The rate of refugee placement in Manchester has remained relatively steady since the 1990s. Of all refugees who arrived in Manchester since 1982, 7 percent arrived during the 1980s, 41 percent during the 1990s, and 40 percent between 2000 and 2010. The authors note that immigration and diversity play important roles in economic growth. However, gaps in education and poverty and language barriers must be addressed if the city is to fully realize the benefits of this demographic change.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.669

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0350.004

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 head0.101
GPT teacher head0.382
Teacher spread0.281 · 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 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
Published2014
Admission routes1
Has abstractyes

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