What’s He At?: A Study of Stereotypes in Newfoundland
Bibliographic record
Abstract
This study was designed to see if individuals in Newfoundland would have stereotypes of \nothers based on where they live in the province. Through the use of an online survey, \n184 participants (164 females and 20 males) indicated their perceptions of an individual \nfrom Newfoundland, who was presented as being from different parts of the province. \nParticipants were recruited from the Grenfell Campus Participant Pool, Facebook and \nthrough emails. Participants received randomly assigned information that the individual \nwas from a specific Newfoundland location (St. John’s, the Bay, or a housing complex), \nand were randomly assigned to see videos of the individual wearing clothing \nrepresentative of the different areas engaged in ambiguous behaviour: unlocking and \nentering a car through a window. Although there were no significant effects from the \nlocation variable, participants had different levels of certainty that the individual was \nbreaking into a car based on the video condition, F(2, 87) = 3.28, p = .042, ηp \n2 = .07. \nOverall, participants thought that if the individual was wearing a brand-name jacket \n(representative of St. John’s) he was more likely to break into a car. This study provides \ninitial evidence that there are sub-stereotypes in Newfoundland which can affect \nperceptions of a potential crime.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".