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Record W3087957653 · doi:10.1177/0098628320959924

What Do Students Think When Asked About Psychology as a Science?

2020· article· en· W3087957653 on OpenAlexaff
Lindsay Richardson, Guy Lacroix

Bibliographic record

VenueTeaching of Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicAcademic and Historical Perspectives in Psychology
Canadian institutionsCarleton University
Fundersnot available
KeywordsPsychologyConceptualizationPsychology of scienceBasic scienceSchool psychologyExperimental psychologyScience educationAssociation (psychology)Differential psychologySport psychologyPsychological researchAsian psychologySocial psychologyApplied psychologyMathematics educationCognitionCognitive psychologySocial scienceSociologyPsychotherapist

Abstract

fetched live from OpenAlex

Research has shown that undergraduate courses in psychology often fail to make students accept the discipline as a science. It may be that explicit instruction is not sufficient to modify students’ conceptualization of psychology as something other than science. The goal of this study was to examine introductory psychology students’ conceptualizations of psychology and science. Five hundred and seventy participants completed a free association task for disciplines that included psychology and other sciences. They also provided ratings for these disciplines on relevant dimensions (e.g., important and scientific) and were asked “Is psychology a science?” Students tended to agree that psychology was a science but rated it to be less scientific than the natural sciences. Moreover, the free association results suggested that psychology was semantically distant from the other sciences. Thus, successful pedagogy will need to focus on conceptual change if students are to accept psychology as a science.

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)0.006
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0050.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.052
GPT teacher head0.459
Teacher spread0.407 · 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.

Study designQualitative
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

Citations11
Published2020
Admission routes1
Has abstractyes

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