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Record W4233122093 · doi:10.4337/9781781007693.00005

Preface and Acknowledgements

2008· book-chapter· en· W4233122093 on OpenAlexaboutno aff

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2008
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsnot available
FundersUniversiteit van AmsterdamEuropean Commission
KeywordsComputer science

Abstract

fetched live from OpenAlex

Flexibility and Employment Security in Europe: Labour Markets inTransition traces how individual workers cope with the increasing dynamics on the labour market and the effects that has for their transition patterns and labour market careers.Through the analysis of unique comparative panel data from 14 European countries during the 1990s up to the early 2000s, and tracking the individuals' career fate over a period of eight years, a number of esteemed authors coming from different origins and angles examine the way workers and governments cope with rising demands to meet the needs for increasing flexibility without endangering income and employment security.They address issues as to whether these changes signal the alleged shift in the employment relationship from 'lifetime employment' to the 'boundaryless' career, as for example the Transitional Labour Market theory contends, and how countries through their institutional set up and policies cope with these changes and try to improve the balance of flexibility and security in their societies.The latter has become a key issue now in the European policy debate under the heading of 'flexicurity' since the EU governments have accepted the principles of 'flexicurity' policies in the Autumn of 2007.It is common among economists to speak about a 'zero-sum game' or the inevitable trade-offs when viewing the relationship between efficiency and equity to which the flexibility-security issue refers, but the authors contend that such trade-offs can be avoided and that a 'positive sum-game' is conceivable.For that purpose they try to draw lessons from 'best policy practices' with a view to transitional labour market and flexicurity approaches in Australia, Canada and Denmark.The book is the outcome of a collaborative effort among a set of researchers in ten countries, eight from within and two from outside the EU.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.690
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.036
GPT teacher head0.213
Teacher spread0.177 · 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 teacher head, not a consensus.

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".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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