Bibliographic record
Abstract
Purpose The purpose of this paper is to quantitatively and qualitatively analyze library marketing research output using select bibliometric indicators with the aim of identifying top-performing countries, subject subthemes, organizations, authors and journals in the area. Design/methodology/approach The present study has examined 520 publications in library marketing, as indexed in Scopus database during 2006–2017. The study has statistically assessed processed publications and citations data into tables in order to ascertain research growth rate trends, global publication output and share, citation impact and distribution of library marketing research by country of publication, international-level collaborating country share of publication, by broad subject areas and preferred media of research communication. The study also provides bibliometric profile of top research organizations and authors in terms of their publications and citation indicators. Findings The body of literature in library marketing research is still very small, highly scattered and has so far registered no growth during the last 12 years, i.e. 2006–2017. Library marketing research is yet to emerge as a popular research area in library and information science. The USA dominates The library marketing research in the world, whereas other top participating countries including India, China, Japan, Canada, etc., are distant cousins. Top research institutions in the area of library marketing across the world include City University of New York, Florida State University, University of Texas at Austin, USA, Loughborough University and University of Sheffield, UK. The major focus of library marketing research was on academic libraries, followed by public libraries and medical libraries. Of late, the use of social media has emerged as an alternative to traditional library marketing techniques. Research limitations/implications The scope of study describing the status of library marketing research is limited to the period 2006–2017. The results of the study should interest researchers in finding solutions to formulating future library strategies and programs for achieving sustainable growth in this area of study. Originality/value The present study in library marketing research should be of interest to researchers in evolving, and formulating theories/best practices/and policies/programs for outreach to current and potential users for promoting library products and services.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.045 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.030 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.081 | 0.036 |
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".