Bibliographic record
Abstract
Social marketing as a concept was developed in the 1970s to help improve overall society and to bring about positive social changes. The concept of social marketing was first presented by Zaltman, Kotler, and Kaufman, in their 1972 book, Creating Social Change. This paper addresses the role of social marketing with specific examples of how social marketing associated with educational research can be applied to school libraries. Social marketing is based on general marketing principles and strategies aimed at selling products and services to consumers but with the purpose of improving society by providing socially relevant information; changing existing actions; and improving individual or group behaviors, attitudes or beliefs; and reinforcing desired behaviors. Since the 1970s, social marketing has been used widely in the United States to promote a variety of pro-social behaviors including: reducing smoking, reducing drug abuse, preventing heart disease, promoting contraceptive use, and promoting organ donation. In recent years the U.S. government has used social marketing to encourage enrollment in the controversial Affordable Health Care program. These marketing approaches are theoretically encased in well-conceived educational and public information programs and management. This paper will provide examples of social marketing research methods and results as used by the presenter in school and public libraries youth services. The paper will likewise highlight resources helpful to school librarians in designing and implementing social marketing strategies.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.200 | 0.057 |
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".