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Record W2916146648 · doi:10.22215/etd/2018-13173

Stereotypes and the Familiar-Stranger: The Role of Previous Contact and Gender Stereotypes on Eyewitness Recall and Recognition Accuracy

2018· dissertation· en· W2916146648 on OpenAlexaff
Lauren Thompson

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsCarleton University
Fundersnot available
KeywordsPsychologyRecallStereotype (UML)Consistency (knowledge bases)Social psychologyEyewitness identificationIdentification (biology)Developmental psychologyCognitive psychology

Abstract

fetched live from OpenAlex

The present study examined the influence of prior familiarity with a perpetrator and gender stereotypes on eyewitness recall and identification accuracy. Participants (N = 257) watched a crime video where the perpetrator was either someone they had never met before (unfamiliar/stranger condition), or was someone with whom they had a 1 minute exposure to prior to the crime (familiar condition). In the familiar conditions the 1 minute exposure included the target talking about their occupation; this occupation was either consistent or inconsistent with gender role stereotypes. There were no significant differences in recall or identification accuracy between the familiar or stereotypes conditions. However, when participants were asked to rate the degree to which they viewed the target as stereotype consistent versus inconsistent, higher stereotype consistency ratings were associated with reporting more total descriptors and higher proportions of correct descriptors. Conversely, lower stereotype consistency ratings predicted more correct identification decisions.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.298
Teacher spread0.255 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations0
Published2018
Admission routes1
Has abstractyes

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