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Record W4396858299 · doi:10.15173/ijsap.v8i1.5417

Collaborative authoring using wiki

2024· article· en· W4396858299 on OpenAlexvenueno aff
James T. Neill

Bibliographic record

VenueInternational Journal for Students as Partners · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceWorld Wide WebMultimedia

Abstract

fetched live from OpenAlex

This case study describes a collaborative online authoring project in which undergraduate students co-produce open educational resources as a learning and assessment exercise. Over 1,500 chapters and videos about how psychological science can improve people’s motivational and emotional lives have been co-created. Wikiversity provides a simple and powerful open editing and hosting platform. The project’s key pedagogical principles include students as partners, open education, guided experiential learning, and self-determined learning. Other key ingredients include scaffolding, skill development, and formative feedback. Potential issues are framed as educational opportunities, including privacy and anonymity, intellectual property and copyright, and individual versus collective work. Collaborative online authoring projects offer real-world benefits over disposable essays in higher education. The principles and methods are adaptable to a wide variety of disciplines and educational contexts, offering a scalable approach to collaborative student-staff partnerships.

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)0.007
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.003
Scholarly communication0.0090.011
Open science0.0030.008
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.003

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 head0.061
GPT teacher head0.628
Teacher spread0.567 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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