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Record W2150183166 · doi:10.3109/0142159x.2012.733838

The application of wiki technology in medical education

2012· review· en· W2150183166 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedical Teacher · 2012
Typereview
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceConstruct (python library)World Wide WebKnowledge managementMedical educationMultimediaMedicine

Abstract

fetched live from OpenAlex

UNLABELLED: BACKGROUND, AIMS AND METHODS: Recent years have seen the introduction of web-based technologies such as the 'wiki', which is a webpage whose content can be edited in real time using a web browser. This article reviews the current state of knowledge about the use of wikis in education, and considers whether wiki technology has features that might prove useful in medical education. RESULTS: Advantages and challenges of the technology are discussed, and recommendations for use are provided. We believe that wiki technology offers a number of potential benefits for administrators, students and instructors, including the ability to share information online, to construct knowledge together, to facilitate collaboration and to enable social learning and peer feedback. CONCLUSIONS: We believe that with proper planning and instructional design, wiki technology can be usefully employed in medical education. We intend to continue to study the impact of wiki technology in our own programme, and we encourage others to evaluate the application of wiki technology in other areas of medical education.

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.

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.037
GPT teacher head0.457
Teacher spread0.420 · 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