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Record W4415682018 · doi:10.65030/idr.06007

The Effectiveness of Psychology Courses at Disconfirming Common Psychological Myths

2008· article· W4415682018 on OpenAlexaboutno aff
Laura A. Glass, Jared M. Bartels, Joseph J. Ryan, Kim Stark-Wroblewski

Bibliographic record

VenueIndividual Differences Research · 2008
Typearticle
Language
FieldPsychology
TopicAcademic and Historical Perspectives in Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsMythologyTest (biology)Sample (material)Psychology of learningCommunity collegeContrast (vision)

Abstract

fetched live from OpenAlex

The authors attempted to generalize Standing and Huber's (2003) findings concerning psychological myth-beliefs among Canadian students to a sample of Midwestern Americans, including university students (n = 171), junior college students (n = 79), and a local community sample (n = 45). Midwesterners' scores on the Huber Test were significantly lower than those of Canadians. In contrast to Standing and Huber's findings, myth acceptance was no higher for junior college than university students. Psychology courses taken and GPA were significant predictors of Huber test scores. Additionally, students were less susceptible to myth acceptance after having taken 10 or more psychology courses compared to those having taken 0 to 1 and 2 to 9 courses.

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 imitation

Not 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.

metaresearch head score (Codex)0.035
metaresearch head score (Gemma)0.203
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.203
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.286
GPT teacher head0.520
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2008
Admission routes1
Has abstractyes

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