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Record W4403877479 · doi:10.1353/lib.2024.a941429

Understanding Knowledge Management Education within the North American Higher Education System

2024· article· en· W4403877479 on OpenAlexaboutno aff
H. Frank Cervone, Kendra Albright

Bibliographic record

VenueLibrary trends · 2024
Typearticle
Languageen
FieldComputer Science
TopicInformation Systems Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsHigher educationKnowledge managementLibrary sciencePolitical scienceEngineering managementSociologyEngineeringComputer science

Abstract

fetched live from OpenAlex

Abstract: The purpose of this article is to provide an overview of the higher educational systems in the United States and Canada in general and knowledge management (KM) education specifically. The terminal degree for KM is primarily considered to be a master’s degree, and programs vary across disciplines. There are very few master’s degrees in KM; these are found in academic departments or schools of information, business administration, professional studies, and policy and government. Details on these degree programs as well as certificates are presented. Previous degree programs that have been discontinued are discussed. KM courses are also listed that are offered in other library and information science programs throughout the United States and Canada but not part of a KM degree program otherwise. It appears that KM programs and courses are in decline, although this may suggest an evolution rather than a decay of the field.

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)0.002
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.966

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.002
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.032
GPT teacher head0.258
Teacher spread0.225 · 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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