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Record W3088775229 · doi:10.4324/9780429352690

Self-Initiated Expatriates in Context

2020· book· en· W3088775229 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)PsychologyGeographyArchaeology

Abstract

fetched live from OpenAlex

SIEs IHRM"So far the literature has focused primarily on the individual perspective of SIEs, on the one hand, or Human Resource Management (IHRM) for international assignments within an organization, on the other hand.Little regard is paid to the opportunities IHRM can play in supporting self-initiated expatriates in their careers, e.g. in terms of adjustment to the new organisation and culture, family support, employment conditions or international career management.This chapter addresses how organizations can contribute to successful employment of selfinitiated expatriates via their organizational career management and general IHRM practices.In view of the context dependency of SIE careers, implications for the management of SIEs are considered."Drawing on the work of Selmer andCerdin (2014) and Vaiman, Haslberger and Vance (2015) we define self-initiated expatriates (SIEs) as individuals who relocate internationally, have the intention to have regular employment, plan to stay in the country temporarily and are skilled/have professional qualifications.SIEs initiate their expatriation and secure a position in another country of their own volition.In other words, the SIE takes responsibility for their employment and career progression rather than relying on organisational support to progress their career.The profile of SIEs aligns to modern career theory whereby individuals are increasingly responsible for the own career and are less reliant on organisational support (Suutari, Brewster, Mäkelä, Dickmann, & Tornikoski, 2018).This has manifested itself in a rise in global mobility, often self-initiated as the section illustrates. Global Talent FlowsGlobal migration, including highly skilled migrants is rising and is likely to continue to do so (OECD, 2017).Skilled migrants are defined as 'highly educated and experienced individuals who have developed skills in diverse occupations such as management, engineering or medicine, among other professions' (Crowley-Henry & Al Ariss, 2018, p. 2057).These individuals continue to be attracted to high-income destinations with better levels of wellbeing, primarily the USA, but also Canada, Australia and the UK, which collectively host 2/3rds of skilled migrants.They are drawn to these destinations because they are English speaking countries with high wages and, in the case of the USA, lower taxes.There has been a doubling of the tertiary-educated, mobile labour force globally and fierce competition globally to attract this talent (Kerr, Kerr, Özden, & Parsons, 2016).As Khilji, Tarique and Schuler (2015, p. 2) note, 'the war for talent has intensified and gone global', with not just organisations competing for scarce talent, but countries too.This is due, at least in part, to an agglomeration effect i.e. a 'worker's productivity is enhanced by being near to or working with many other skilled workers in similar sectors or occupations ' (Kerr et al., 2016, p. 92).At a macro-level, governments act as gatekeepers to talent.For example, many countries have removed restrictions to attract global talent.For instance, the Start-Up Chile scheme pays foreign entrepreneurs to spend 6 months in the country to build a diaspora and develop global skill connections.Malaysia has a Residence Pass Talent Programme to attract foreign talent to the country.In The Netherlands, the new Expatcenter Entry Procedure has been established to attract high skilled migrants (Kerr, Özden, & Parsons, 2017).Other intermediaries enable the movement of skilled migrant labour, such as recruitment agencies and, also government department-led initiatives facilitate these talent flows for example, The Federal Skilled Worker Program in Canada (Harvey, Groutsis, & Van den Broek, 2018).At a meso-level, organisations are also seeking to attract

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.850
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.062
GPT teacher head0.333
Teacher spread0.271 · 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; both teacher heads agree on what is shown here.

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

Citations19
Published2020
Admission routes1
Has abstractyes

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