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Record W2049650557 · doi:10.5964/ejop.v2i4.289

Enhancing the success of outsourcing initiatives

2006· article· en· W2049650557 on OpenAlexaffabout
Carroll Hern, Ronald J. Burke

Bibliographic record

VenueEurope’s Journal of Psychology · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsYork University
Fundersnot available
KeywordsOutsourcingBusinessService (business)OffshoringCore competencyHuman resourcesMarketingInformation technologyOperations managementIndustrial organizationManagementEconomicsComputer science

Abstract

fetched live from OpenAlex

Outsourcing has become an increasingly powerful option for many companies seeking to reduce costs, enhance service and focus on core competencies. Most commonly used for information technology services, this trend of business process outsourcing (BPO) is very prevalent in Human Resources (HR). It is a $1.36 billion industry in Canada alone and is expected to increase 10.6 percent each year over the next five years (Vu, 2004). According to the Gartner Group, HR administrative tasks topped the list of processes outsourced in 2003 and predict that by 2007, HR BPO will be a $37.8 billion-plus industry in the United States, up from $25 billion in 2002, for an 8.6 percent growth rate.

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)0.013
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.003
Scholarly communication0.0090.005
Open science0.0010.011
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.003

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 head0.015
GPT teacher head0.262
Teacher spread0.247 · 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 designObservational
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

Citations5
Published2006
Admission routes2
Has abstractyes

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