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Record W4221004633 · doi:10.1007/s43076-022-00168-5

International Capacity Building in Psychological Science: Reflections on Student Involvement and Endeavors

2022· article· en· W4221004633 on OpenAlexaff
Daniel Balva, Daniel Thomas Page, Fanie Collardeau, Julio Andrés Gómez Henao, Ana Lorena Flores-Camacho

Bibliographic record

VenueTrends in Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPsychological scienceCapacity buildingPsychologyEngineering ethicsMathematics educationPedagogySociologyPolitical scienceEngineeringSocial psychology

Abstract

fetched live from OpenAlex

Internationalization in psychology provides unique opportunities for students worldwide and promises to build a more inclusive, representative, and culturally sensitive discipline. Far from passive recipients of the internationalization process, students are actively involved in promoting opportunities for cross-cultural collaborations, international learning, and the creation of international networks. This paper reviews opportunities for student involvement in internationalization related efforts in psychology. Students' roles within international and regional psychology organizations are explored to highlight the unique contributions and opportunities afforded by more independent and fully student-led organizations and initiatives. This paper discusses the barriers to establishing student-led organizations and to student involvement in international endeavors, including power imbalances, language barriers, and disparities in students' ability to access financial resources and mentorship depending on their geographical location. Recommendations are offered, to both students and professional members, to foster student contributions to the internationalization of psychology and support the creation of sustainable student-led international organizations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.295
GPT teacher head0.548
Teacher spread0.253 · 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.

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

Citations7
Published2022
Admission routes1
Has abstractyes

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