A survey of assessor beliefs and practices related to faking
Bibliographic record
Abstract
Purpose To gather information on assessor beliefs and behaviors in relation to assessee faking issues on a personality inventory in the individual assessment process. Design/methodology/approach A survey approach was used in this research. Totally 77 experienced assessors who conducted individual assessments for an international consulting firm responded to the survey. Analyses of mean item rankings were used to answer several research questions. Findings Major results of the study were: assessors believe faking is a problem; assessors believe they can detect faking; and assessors believe they can effectively eliminate all of the effects of faking when evaluating the candidates. Practical implications The first implication from this research is that assessors believe that they can detect and deal with faking despite a paucity of evidence to support it. The second implication is that organizations may be reluctant to continue to develop effective methods of identifying and dealing with faking if their assessors mistakenly believe they are already successfully doing so. Originality/value This study is the first to survey experience assessors regarding their beliefs and perceptions of faking issues in the individual assessment process and is designed to garner immediate practical insights and ideas for future testable hypotheses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".