Leximancer Software as a Research Tool for Social Marketers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The amount of human effort required to do content analysis research “by hand” is often time-consuming, and unreliability is a common concern. Our aim was to conduct a content analysis that traces the history of Social Marketing Quarterly ( SMQ) articles by using Leximancer (version 4.5)—a software tool designed for analyzing natural-language text data. We adhered to Krippendorff’s network of steps to address two research questions: (1) “What are the prevailing conceptualizations of the application of social marketing?” and (2) “How have those conceptualizations changed over time?” We identified all SMQ volumes/issues published between May 1994 (inaugural issue) and September 2015. Our sampling units consisted of all SMQ “Application” articles published during that time ( n = 162). Leximancer output includes a conceptual map representing the main concepts within the text and how they are related (themes). Based on conceptual and relational analyses, one would surmise that social marketing applications (e.g., campaigns) predominantly address health-related problems through behavioral influence strategies, informed by audience research and designed to include the elements of the marketing mix (e.g., messaging). The predominant health topic addressed by social marketing applications has been tobacco use and smoking. Leximancer has a number of desirable features including an ability to quickly handle large amounts of text in various formats and languages. However, those features are no substitute for a content analysis design that makes the research reproducible and available for critical examination—a shortcoming of previous content analyses of the social marketing 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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.019 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it