LinkedIn‐based assessments of applicant personality, cognitive ability, and likelihood of organizational citizenship behaviors: Comparing self‐, other‐, and language‐based automated ratings
Bibliographic record
Abstract
Abstract We compared self‐reports or test‐based assessments of personality, cognitive ability, and likelihood or tendencies to engage in organizational citizenship behaviors (OCB) from experienced workers ( targets , N = 154) with one approach to rate these traits based on LinkedIn profiles using hiring professionals ( panel raters , N = 200), graduate students in Industrial‐Organizational Psychology ( I‐O raters, N = 6), and automated assessments with the language‐based tool Receptiviti (for personality only). We also explored the potential for adverse impact associated with this approach of LinkedIn profile assessments and how profile elements are associated with ratings. Results demonstrated that raters can reliably assess personality, cognitive ability, and OCB with one‐item measures. LinkedIn showed little promise for valid assessments of personality (except some weak evidence for honesty‐humility) and OCB tendencies for all data sources. And, we only found modest evidence of convergent validity for cognitive ability. Automated assessments of personality with Receptiviti were more consistent with raters' assessments than targets' self‐reports. LinkedIn‐based hiring recommendations did also not differ on the basis of gender, race, or age. Finally, in terms of profile content, longer LinkedIn profiles with more professional connections, more skills listed, or including a professional picture were viewed more positively by both types of raters. But these content elements were largely unrelated to targets' self‐reports or test scores. Thus, organizations should be careful when relying on LinkedIn‐based assessments of applicants' traits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".