Selecting Suitable Job Quality Items in Profiling and Job Matching Algorithms for Public Employment Services

Clément Brébion; Janine Leschke; Pavle Boskoski; Biljana Mileva Boshkoska

Selecting Suitable Job Quality Items in Profiling and Job Matching Algorithms for Public Employment Services

Clément Brébion, Janine Leschke, Pavle Boskoski, Biljana Mileva Boshkoska

Department of Management, Society and Communication

Publikation: Konferencebidrag › Paper › Forskning › peer review

Abstract

This paper takes its starting point in the lessons learned from first generation statistical profiling systems in Public Employment Services (PES) and the sociological vision of labour market integration and good-quality work. Commonly job quality is not considered in statistical profiling, instead exit to any type of employment is modelled. We discuss and operationalize the multi-dimensional concept of job quality with a view on whether and how job quality items can be integrated into a tool computing the probability of exit into good jobs.

Originalsprog	Engelsk
Publikationsdato	2022
Antal sider	36
Status	Udgivet - 2022
Begivenhed	20th Annual European Network for Social Policy Analysis Conference. ESPAnet 2022: Social Policy Change between Path Dependency and Innovation - Wien, University of Vienna, Østrig Varighed: 14 sep. 2022 → 16 sep. 2022 Konferencens nummer: 20 https://www.espanet-vienna2022.org/

Konference

Konference	20th Annual European Network for Social Policy Analysis Conference. ESPAnet 2022
Nummer	20
Lokation	Wien
Land/Område	Østrig
By	University of Vienna
Periode	14/09/2022 → 16/09/2022
Internetadresse	https://www.espanet-vienna2022.org/

Emneord

Algorithms
Job quality
Matching
Profiling
Public employment services

Handle.net

Persistent link

Selecting Suitable Job Quality Items in Profiling and Job Matching Algorithms for Public Employment Services
Brébion, C. (Foredragsholder)
29 nov. 2022
Aktivitet: Tale eller præsentation › Foredrag og mundtlige bidrag
Selecting the Suitable Job Quality Items in Profiling and Job Matching Algorithms for Public Employment Services
Brébion, C. (Foredragsholder)
18 maj 2022
Aktivitet: Tale eller præsentation › Foredrag og mundtlige bidrag

Citationsformater

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title = "Selecting Suitable Job Quality Items in Profiling and Job Matching Algorithms for Public Employment Services",

abstract = "This paper takes its starting point in the lessons learned from first generation statistical profiling systems in Public Employment Services (PES) and the sociological vision of labour market integration and good-quality work. Commonly job quality is not considered in statistical profiling, instead exit to any type of employment is modelled. We discuss and operationalize the multi-dimensional concept of job quality with a view on whether and how job quality items can be integrated into a tool computing the probability of exit into good jobs.",

keywords = "Algorithms, Job quality, Matching, Profiling, Public employment services, Algorithms, Job quality, Matching, Profiling, Public employment services",

author = "Cl{\'e}ment Br{\'e}bion and Janine Leschke and Pavle Boskoski and Boshkoska, {Biljana Mileva}",

year = "2022",

language = "English",

note = "20th Annual European Network for Social Policy Analysis Conference. ESPAnet 2022 ; Conference date: 14-09-2022 Through 16-09-2022",

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Selecting Suitable Job Quality Items in Profiling and Job Matching Algorithms for Public Employment Services. / Brébion, Clément ; Leschke, Janine; Boskoski, Pavle et al.
2022. Paper præsenteret på 20th Annual European Network for Social Policy Analysis Conference. ESPAnet 2022, University of Vienna, Østrig.

Publikation: Konferencebidrag › Paper › Forskning › peer review

TY - CONF

T1 - Selecting Suitable Job Quality Items in Profiling and Job Matching Algorithms for Public Employment Services

AU - Brébion, Clément

AU - Leschke, Janine

AU - Boskoski, Pavle

AU - Boshkoska, Biljana Mileva

N1 - Conference code: 20

PY - 2022

Y1 - 2022

N2 - This paper takes its starting point in the lessons learned from first generation statistical profiling systems in Public Employment Services (PES) and the sociological vision of labour market integration and good-quality work. Commonly job quality is not considered in statistical profiling, instead exit to any type of employment is modelled. We discuss and operationalize the multi-dimensional concept of job quality with a view on whether and how job quality items can be integrated into a tool computing the probability of exit into good jobs.

AB - This paper takes its starting point in the lessons learned from first generation statistical profiling systems in Public Employment Services (PES) and the sociological vision of labour market integration and good-quality work. Commonly job quality is not considered in statistical profiling, instead exit to any type of employment is modelled. We discuss and operationalize the multi-dimensional concept of job quality with a view on whether and how job quality items can be integrated into a tool computing the probability of exit into good jobs.

KW - Algorithms

KW - Job quality

KW - Matching

KW - Profiling

KW - Public employment services

KW - Algorithms

KW - Job quality

KW - Matching

KW - Profiling

KW - Public employment services

M3 - Paper

T2 - 20th Annual European Network for Social Policy Analysis Conference. ESPAnet 2022

Y2 - 14 September 2022 through 16 September 2022

ER -

Selecting Suitable Job Quality Items in Profiling and Job Matching Algorithms for Public Employment Services

Abstract

Konference

Emneord

Handle.net

Aktiviteter

Selecting Suitable Job Quality Items in Profiling and Job Matching Algorithms for Public Employment Services

Selecting the Suitable Job Quality Items in Profiling and Job Matching Algorithms for Public Employment Services

Citationsformater