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Record W4389120830 · doi:10.21432/cjlt28558

Editorial

2023· editorial· en· W4389120830 on OpenAlexaffvenueabout
Martha Cleveland‐Innes

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2023
Typeeditorial
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsAthabasca University
Fundersnot available
KeywordsBATESContext (archaeology)Library scienceSociologyLifelong learningDistance educationBest practiceHigher educationPolitical sciencePedagogyPublic relationsEngineering ethicsEngineeringComputer science

Abstract

fetched live from OpenAlex

This next Canadian Journal of Learning and Technology issue is published on the heels of the well-attended ICDE (International Council for Open and Distance Education) conference. The Conference’s overlapping topics and attendant researchers, well-known to this journal, remind us that our field is important, well-subscribed, growing, and changing. An excellent overview of this ICDE Conference and information about the state of education transformation in the current global context can be found here in recent blog posts by the well-known expert and author on the topic of education and technology, Dr. Tony Bates. Learning and technology, the focus of research published by CJLT, is a microcosm in the much larger fields of open, distance, and digital education. Research spans all sectors: primary, secondary, post-secondary, higher education, and lifelong learning. Across issues and years, we seek to touch on the research, theory, and practice in these areas, particularly those where authors are in, or research topics are relevant to, Canada. Canadian researchers were well-represented at the recent ICDE conference, and a Canadian researcher received the conference’s best paper award!

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 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)0.002
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.064
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.289
Teacher spread0.282 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEditorial

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
Published2023
Admission routes3
Has abstractyes

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