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Record W4214855088 · doi:10.46692/9781447306450.001

Introduction: social policy concepts and language

2014· other· en· W4214855088 on OpenAlexaff
Daniel Béland, Klaus Petersen

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceLinguisticsSociologyPhilosophy

Abstract

fetched live from OpenAlex

Exploring social policy language and concepts Social scientists, historians, and linguists have noted that the terms, metaphors, and concepts we use are far from innocent and are closely tied to political struggles and international exchanges. Therefore, from a comparative and international perspective, studying terminology and concept formation is an important part of both political and policy analysis (Williams, 1976; Sartori, 1984; Farr, 1989; Heywood, 2000; Daigneault, 2012). This is also the case when it comes to social policy. The words we use to make sense of social policy and the way we use them need to be properly studied to get the definitions right while grasping the political consequences of social policy language. But so far, relatively little has been done in that respect. In recent years, researchers have rightly complained about the vagueness of core concepts used in contemporary social policy debates. For example, following well-known predecessors such as Asa Briggs (1973–74, 1985) and Richard Titmuss (1963), John Veit-Wilson (2000, 2003) and Daniel Wincott (2001, 2003) have criticised the tendency among social policy students and practitioners to use the concept of ‘welfare state’ without offering any coherent definition of it. As Wincott (2001: 409) puts it, ‘While the expression of “the welfare state” has many interpretations and connotations – both academic and popular – there are surprisingly few clear discussions of the concept… . [The] field does need to be mapped’. Social policy concepts are subject to many interpretations, partly because they are dynamic historical constructions. Thus, one way to map this field and explore the boundaries of social policy is to take a comparative look at the history of concepts like ‘welfare state’ (Beland, 2011; Petersen and Petersen, 2013). Even though a number of key social policy concepts (such as ‘ Wohlfahrtsstat ,’ ‘ Sozialstaat ,’ or ‘ Sozialpolitik ’) have a German origin, most of the English language discussions on social policy language (for example in Flora and Heidenheimer, 1981; Alber, 1988; Lowe, 1999; Powell and Hewitt, 2002; Titmuss 1963) focus on Great Britain , as it was British Archbishop William Temple (1941) and others who popularised the term ‘welfare state’ during the Second World War, and paved the way for its political breakthrough in the late 1940s (Figure I.1).

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.039
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0040.009
Scholarly communication0.0080.009
Open science0.0010.003
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0390.008

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 head0.014
GPT teacher head0.352
Teacher spread0.339 · 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 designTheoretical or conceptual
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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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