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Record W2321612632 · doi:10.1057/9781137347015_3

Labour, Poverty and the Export of Destitute Children As ‘Waste’

2014· book-chapter· en· W2321612632 on OpenAlexaboutno aff
Francesca Ashurst, Couze Venn

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2014
Typebook-chapter
Languageen
FieldSocial Sciences
TopicAustralian History and Society
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyContext (archaeology)ImmigrationPoliticsColonialismPolitical economyPopulationPolitical scienceCapital (architecture)State (computer science)Development economicsEconomyGeographyLawSociologyEconomics

Abstract

fetched live from OpenAlex

We have been arguing that a close connection began to be established between early forms of exclusion and the interests of capital. The context is the rise to predominance of the ideas of classical liberal political economy in Britain, particularly those of Smith, Bentham and Malthus, ideas which increasingly shaped policy and opinion. Central to these changes were the relocation and reconceptualisation of the poor, and the working class generally, by reference to the ‘wealth of the nation’ and to State regulation and management of targeted populations. Thus, political economy became authoritative in the debates leading up to the changes to the Poor Law Acts (1834), with direct consequences for the population of children we are considering. This chapter uncovers the importance of colonial economy in determining national strategies and policies regarding the treatment of the poor and those who fell foul of the law because of poverty. Immigration, notably from Ireland to England, and migration, forced or otherwise, settler colonies of Australia, Canada and South Africa, were central to these strategies. There is thus a global and imperial scope to the problem of exclusion which tends to be neglected, in this case the exportation to the colonies of troublesome and troubled children. This element of the genealogy has implications for a fuller account of biopolitics which will be noted at the end of the chapter. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.018
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.017
GPT teacher head0.246
Teacher spread0.230 · 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 designQualitative
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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Same venuePalgrave Macmillan UK eBooksSame topicAustralian History and SocietyFrench-language works237,207