Putting bias into context: The role of familiarity in identification.
Bibliographic record
Abstract
Previous demonstrations of context effects in the forensic comparison sciences have shown that the number of "match" responses a person makes can be swayed by case information. Less clear is whether these effects are a result of changes in accuracy (e.g., discrimination ability), a shift in response bias (e.g., tendency to say "match" or "no match") or a mix of the 2. We present a series of experiments where we use a signal detection framework to examine the effects of case information (separately) on forensic comparison accuracy and response bias. We also explore the role of familiarity as 1 potential mechanism for case information to sway accuracy. In Experiment 1, case information about crimes perceived to be more severe swayed people to say "match" more, but had little bearing on their ability to discriminate matching and nonmatching fingerprint pairs. In Experiment 2, case information did affect accuracy when it was familiar (i.e., if a previous similar case was associated with a "match" then people were more likely to also rate the current case as a "match," even though it was not). Even when we blinded people to all extrinsic case information in Experiment 3, accuracy was significantly affected by the familiarity of the fingerprints. These results demonstrate that contextual factors can have different (and independent) influences on accuracy and response bias and that even subtle information can affect accuracy if it is sufficiently similar to the case or trace at hand.
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 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 | 0.000 | 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) | 0.000 | 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".