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Record W4225096490 · doi:10.26685/urncst.361

The University of Toronto Scarborough Psychology and Neuroscience Departmental Students' Association (PNDA) 2022 Academic Research Panel Conference Booklet

2022· article· en· W4225096490 on OpenAlexafffundabout
Ilakkiah Chandran, Mahnoor Khan

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersUniversity of Toronto ScarboroughUniversity of Toronto
KeywordsExcellencePsychologyMental healthPanel discussionAssociation (psychology)Medical educationUndergraduate researchMedicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

The Psychology and Neuroscience Departmental Students’ Association (PNDA) advocates on behalf of its members to the Department of Psychology at University of Toronto Scarborough and fosters academic excellence and career growth. PNDA provides academic and professional support by creating opportunities to interact and network with like-minded individuals while serving as a hub for all matters pertaining to the Psychology, Mental Health studies, and Neuroscience programs, thereby connecting members, students, faculty, staff, organizations, institutions, companies, and communities. The Academic Research Panel (ARP) is an annual event focused on fostering research and student engagement amongst UTSC students specifically in the psychology, neuroscience, and mental health studies programs. Each year the ARP is led by undergraduate students from PNDA providing students with a platform to network and showcase their scientific work. This booklet is composed of abstracts from the presenting undergraduate students.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.995
Threshold uncertainty score0.649

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.5450.247

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 head0.156
GPT teacher head0.469
Teacher spread0.313 · 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 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
Published2022
Admission routes3
Has abstractyes

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