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Record W4387665355 · doi:10.15173/ijsap.v7i2.5363

Student partnership in creating an event: Benefits, challenges, and outcomes

2023· article· en· W4387665355 on OpenAlexvenueno aff
Eliza Kitchen

Bibliographic record

VenueInternational Journal for Students as Partners · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsRubricGeneral partnershipEvent (particle physics)Experiential learningTeamworkProcess (computing)PsychologyKnowledge managementWork (physics)Event managementMedical educationComputer sciencePedagogyEngineeringBusinessMedicinePolitical scienceCritical success factor

Abstract

fetched live from OpenAlex

This case study explores a partnership within an events management topic. Students were encouraged to take ownership over the creation and operation of an event held on the university campus. The topic lecturer provided guidance throughout the process and liaised with the students to define the assessment and the marking rubric for the event project. Research data was captured through two surveys—one during the event project and one after the event project was completed. The surveys captured quantitative and qualitative data about students’ perceptions on the benefits, challenges, and outcomes of this experiential learning experience. Survey findings indicated that communication and teamwork were key aspects that needed to be managed to effectively collaborate on the project. Classroom discussion and online communication tools were used to share ideas and information, and work towards the common goals of the event. Through this project, students developed their relationships with their peers and university staff and felt that they had a valuable learning experience that helped to connect theory with practice.

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)0.023
metaresearch head score (Gemma)0.031
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0140.006
Scholarly communication0.0150.008
Open science0.0030.021
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0060.002

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 head0.193
GPT teacher head0.595
Teacher spread0.402 · 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
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

Citations1
Published2023
Admission routes1
Has abstractyes

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