MétaCan
Menu
Back to cohort
Record W2940953466 · doi:10.1111/ijsa.12243

Factors affecting compliance with reference check requests

2019· article· en· W2940953466 on OpenAlexaff
Cynthia A. Hedricks, Disha D. Rupayana, Peter A. Fisher, Chet Robie

Bibliographic record

VenueInternational Journal of Selection and Assessment · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEmployer Branding and e-HRM
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsCompliance (psychology)PsychologyReference dataReference modelReference valuesSample (material)Selection (genetic algorithm)Applied psychologyComputer scienceSocial psychologyDatabaseMedicine

Abstract

fetched live from OpenAlex

Abstract Structured reference checks have been demonstrated to be a reliable and valid predictor of job performance. However, the reference check is a unique assessment method for personnel selection in that a third party, the reference provider, is the source of the critical information on the candidate. If a reference provider is unwilling to either partially or fully comply with a reference check request, the usefulness of the reference check is likely to be compromised. This study examined the factors affecting compliance of the potential reference provider with a reference check request. The sample consisted of 905 U.S. adults who were recruited through the Prolific online crowdsourcing platform ( https://prolific.ac ). We asked the participants a series of questions related to their actual experiences over the past year in responding to reference check requests. We also included a between‐subjects scenario that examined whether job candidate performance, relationship to job candidate, and method of providing the employment reference would affect hypothetical compliance. The results of the study can be used to more deeply understand the factors that are related to the compliance of the reference provider, and as a result, more fully understand the value of the reference check for selection.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.271

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.063
GPT teacher head0.329
Teacher spread0.266 · 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.

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

Citations13
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Selection and AssessmentSame topicEmployer Branding and e-HRMFrench-language works237,207