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Record W3144883319 · doi:10.29173/iasl7871

Developing Online Master's Programs for Teacher-Librarians

2021· article· en· W3144883319 on OpenAlexaffvenueabout
Dianne Oberg

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsAlberta Advanced EducationUniversity of Alberta
Fundersnot available
KeywordsDistance educationVideoconferencingSchool libraryPedagogySociologyMathematics educationFace (sociological concept)Library scienceComputer scienceMultimediaPsychologySocial science

Abstract

fetched live from OpenAlex

Over the past four decades, in Australia, Canada and the USA, school library education at a distance has been delivered though three primary modes: (1) correspondence study; (2) two-way or interactive television and videoconferencing; and (3) Web-based onlinelearning management systems. The theoretical foundations of distance education emphasize that the particular technology or mode of distance education is not as important as the pedagogy employed. Major pedagogical approaches evident in school library education at a distance are: behaviourist/cognitivist; constructivist; and connectivist. This brief history of school library education at a distance focuses on efforts to free school library education from the bounds set by the traditional location and scheduling of library education--on-campus, in universities in cities, with regularly scheduled face-to-face meetings, most amenable to fulltime students. Today’s school library education at a distance is primarily an anytime/anyplace endeavour that is attractive to part time students who are employed full time.

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)0.003
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.115
Threshold uncertainty score0.384

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0020.001
Scholarly communication0.0040.004
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1150.044

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 head0.092
GPT teacher head0.342
Teacher spread0.250 · 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
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".

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Citations0
Published2021
Admission routes3
Has abstractyes

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