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Record W3088814927 · doi:10.4324/9781003073413-4

Politics and Ethnic Minorities

2020· book-chapter· en· W3088814927 on OpenAlexaboutno aff
Danièle Joly, Cathie Lloyd, David Owen

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicPost-Soviet Geopolitical Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupPoliticsPolitical sciencePolitical economySociologyLaw

Abstract

fetched live from OpenAlex

The economic boom of the post-war reconstruction of Britain brought about the arrival of numerous immigrant workers as a response to labour shortages. According to the 1991 Census (Country of Birth, Great Britain) 3.8 million people in Britain were bom overseas. However, almost half of these (1.81 million) are from Ireland, Australia, New Zealand, Canada and other countries, including Western Europe. The other 1.96 million were bom mostly in New Commonwealth countries and Pakistan. 1 The establishment of ethnic minorities of immigrant origin is a recent phenomenon in Britain which began substantially at the end of the Second World War and in the wake of decolonisation. The implications of this migratory flow had not been foreseen and led to the emergence of laws, policies and changes in the social fabric which are summarised in this chapter.

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)0.001
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0050.002
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.002

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 head0.049
GPT teacher head0.312
Teacher spread0.263 · 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
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

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