Marketing Competency for Information Professionals: The Role of Marketing Education in Library and Information Science Education Programs
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".