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Record W3166732086 · doi:10.1111/ijsa.12335

An updated survey of beliefs and practices related to faking in individual assessments

2021· article· en· W3166732086 on OpenAlexaff
Chet Robie, Stephen D. Risavy, Rick Jacobs, Neil Douglas Christiansen, Cornelius J. König, Andrew B. Speer

Bibliographic record

VenueInternational Journal of Selection and Assessment · 2021
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPsychologyPersonalitySample (material)Applied psychologySocial psychologyBig Five personality traitsSurvey researchContext (archaeology)

Abstract

fetched live from OpenAlex

Abstract The present study is an updated survey examining individual assessor beliefs and practices related to faking in the individual assessment context. The responses from a mix of quantitative and qualitative survey questions were compared across individual assessors from the original 2005 sample (n = 77) and an updated 2020 sample (n = 78). Results suggest that single stimulus personality assessments are still the predominant form of personality assessment in use, but many individual assessors employ other types of personality assessments such as forced‐choice. In 2020, individual assessors do not appear to be heavily concerned about the effects of faking on their recommendations, do not believe that a large number of candidates fake, and believe that even fewer candidates successfully fake.

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)0.008
metaresearch head score (Gemma)0.024
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.076
GPT teacher head0.498
Teacher spread0.422 · 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

Citations13
Published2021
Admission routes1
Has abstractyes

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