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Record W4306752143 · doi:10.15173/ijsap.v6i2.5059

Building co-curricular learning experiences: Lessons from a student-driven experiential learning initiative underpinned by a student-staff collaborative model

2022· article· en· W4306752143 on OpenAlexvenueno aff
Man Ho Adrian Lam, Anfield Tam Ching Hin

Bibliographic record

VenueInternational Journal for Students as Partners · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningGovernment (linguistics)AutonomyPublic relationsPedagogyStudent engagementPolitical scienceFlexibility (engineering)SociologyManagement

Abstract

fetched live from OpenAlex

In this case study, both authors look into their experiences with a student-driven experiential learning initiative in the form of an academic services committee. The committee embodies a developing community of undergraduate students reading the double degrees in government and laws and also aims at promoting the scholarly study of public affairs and jurisprudence within a traditional and comprehensive research-intensive Hong Kong university. As the flagship student community of the double degree programme, the committee provides tailor-made services for current students and connects them to alumni networks. The authors critically evaluate the multipurpose nature of the committee by applying the five criteria for successful staff-student collaborations, namely (a) reciprocal trust and respect, (b) self-efficacy, (c) flexibility and autonomy, (d) commitment, as well as (e) ownership and responsibility (Martens et al., 2019). Within a synergic collaborative model, students can assume a leadership role in creating and managing multidisciplinary co-curricular learning experiences, with faculty members only assuming indirect, non-voting, and advisory roles under limited circumstances.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
grokno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
opusno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
models agreeAgreement compares identical category sets and study designs across arms.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.079
GPT teacher head0.557
Teacher spread0.478 · 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

Labeled directly by 3 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2022
Admission routes1
Has abstractyes

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