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Record W2909663687

Marketing Competency for Information Professionals: The Role of Marketing Education in Library and Information Science Education Programs

2017· article· en· W2909663687 on OpenAlexaboutno aff
Rajesh Singh

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Marketing Education
Canadian institutionsnot available
Fundersnot available
KeywordsMarketing scienceBusinessMarketingMarketing researchInformation scienceMedical educationMarketing managementPublic relationsPolitical scienceLibrary scienceRelationship marketingMedicineComputer science
DOInot available

Abstract

fetched live from OpenAlex

Marketing is recognized as an important competency for information professionals. However, most library and information science (LIS) schools still fall short when it comes to offering a separate marketing course on a regular basis. Even though marketing has been a popular topic in the LIS profession, some information professionals still have sparse or erroneous perceptions about marketing. Consequently, due to a narrow worldview, they perceive marketing to be a tool for “buying and selling” or solely as a promotional tool. This paper makes the case for LIS schools to provide thorough education and training in marketing for future information professionals. In keeping with this goal, a review of the online marketing curricula of 60 American Library Association-accredited graduate schools in the United States and Canada demonstrates the current landscape of LIS marketing education in relation to the demand for marketing skills and the increasing significance of these competencies for information professionals. Qualitative findings from student reflections on a marketing course suggest that marketing education and training can be immensely powerful in laying a strong foundation of marketing knowledge for information professionals. It is vitally important for LIS schools to bridge the existing gaps in marketing education to meet the professional demands for marketing and associated skills.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0050.073
Open science0.0020.001
Research integrity0.0000.000
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 head0.063
GPT teacher head0.454
Teacher spread0.391 · 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 designObservational
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

Citations2
Published2017
Admission routes1
Has abstractyes

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