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Psi Chi in Global World: Vision of 9 International Chapters

2019· article· en· W2960588726 on OpenAlexaff
Harold Takooshian, Yarielizmar Nieves-Lebrón, Bernadette M. Delgado-Acosta, Pagán Daniela Talavera, Nelson D. Cruz‐Bermúdez, Andrew M Rivers, María del Pilar Grazioso, Marinés Mejía, Diana Williams, Grace Sooknanan, Alicia Hosein, Valeriia S. Tarkhova, Sayad Z. Kardashova, Yu V Kozhukhova, Tsee Leng Choy, Kyle Danielson, Marios Adonis, Yianna Ioannou, Ester Lima

Bibliographic record

VenueRUDN Journal of Psychology and Pedagogics · 2019
Typearticle
Languageen
FieldPsychology
TopicSocial Representations and Identity
Canadian institutionsThe Scarborough HospitalUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsHonorPsychologySociologySocial scienceComputer science

Abstract

fetched live from OpenAlex

Since 2009, when Psi Chi became the International Honor Society in Psychology, outstanding faculty and students across many nations have stepped forward to join the Psi Chi family of 750000 life members in 1130 schools on six continents. How is the Psi Chi experience changing lives around the world, as it has been doing since 1929 in the USA? Here, for the first time, Psi Chi “pioneers” in 9 international Chapters share in their own words and images their Psi Chi experiences.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.029
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0090.007
Open science0.0010.007
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0290.005

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 head0.044
GPT teacher head0.467
Teacher spread0.423 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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