Trust but verify: The biasing effects of witness opinions and background knowledge in workplace investigations
Bibliographic record
Abstract
Introduction: A unique feature of workplace investigations is the familiarity that investigators and witnesses have with the factors involved in the adverse incident. Familiarity creates expectations that can shape investigators' and witnesses' assumptions and opinions. The current research examined the biasing effect of non-factual witness claims on investigators' judgments. These claims, which we call 'uncheckable', included opinions about factors involved in the event and the future. We also examined how participants' a priori knowledge of an employee's history influenced their judgments.Method: This experiment used a 2 (background information: control or unsafe) x 2 (uncheckable content: neutral or unsafe) between-subjects design. Participants were provided with background information about a worker (control or unsafe history) and a witness statement about a workplace event that contained uncheckable claims (neutral or worker as unsafe). We tested how our manipulations biased participants' judgments of (i) the cause of the event, (ii) the witness's confidence and credibility, and (iii) the diagnosticity of the witness's account. We also tested if biasing background information affected how factual participants found the witness’s statement.Results: Biasing uncheckable information (i.e., opinions) affected participants' judgments of event cause (ηp2 = .033) and increased their ratings of witness confidence (ηp2 = .074). Biasing background information about a worker affected participants' judgments of the cause of the event (ηp2 = .088), the diagnostic value of the witness statement (ηp2 = .054), and the number of factual claims in the witness statement, resulting in more uncheckable claims being misclassified as potential facts (ηp2 = .18). Conclusion: This experiment demonstrated the significant effect that non-factual witness statements and irrelevant background information can have on the interpretation of evidence and judgments about the cause of events.Practical Application: Understanding how contextual information can bias investigative judgment helps workplace investigators manage its influence in their judgment practices.
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 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 | 0.027 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".