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eCampusAlberta

2013· book-chapter· en· W2486825387 on OpenAlexaboutno aff
Tricia Donovan, Janet Paterson-Weir

Bibliographic record

VenueIGI Global eBooks · 2013
Typebook-chapter
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsTransformative learningGeneral partnershipStrategic partnershipPolitical scienceExecutive directorLibrary sciencePublic relationsOnline learningMedical educationManagementPublic administrationSociologyWorld Wide WebPedagogyComputer scienceMedicine

Abstract

fetched live from OpenAlex

eCampusAlberta is one of the fastest growing online consortia in North America. It currently provides over sixty credentials fully online to learners in hundreds of communities across Alberta, Canada. Developed in 2002, eCampusAlberta is a consortium of fifteen publicly funded colleges, polytechnics, and universities in western Canada. This strategic partnership was developed by senior executives across the institutions in an effort to increase access to online learning opportunities province-wide. The consortium leveraged existing networks of senior executive officers and informed leaders across the member institutions to build a framework to support the implementation of the consortium. Since its inception, eCampusAlberta has inspired collaboration across member institutes and has had a significant transformative effect on the post-secondary landscape in Alberta. To date, over 47,000 learners have participated in courses offered via the consortium.

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)0.000
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.994
Threshold uncertainty score0.929

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0030.001
Scholarly communication0.0070.004
Open science0.0010.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.2780.156

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 head0.017
GPT teacher head0.240
Teacher spread0.223 · 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
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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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