Identification of Topics and Their Evolution in Management Science: Replicating and Extending an Expert Analysis Using Semi-Automated Methods
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
Latent Dirichlet allocation (LDA) is a popular generative probabilistic model that enables researchers to analyze large semantic datasets; however, few open-source software tools with Graphical User Interfaces (GUIs) are available to researchers. This study identifies an open-source software tool that, in conjunction with a popular electronic spreadsheet software application, can be used to perform topic modeling. A process is developed and evaluated against a pre-existing expert review that examines work published in Management Science on the topics of technological innovation, product development, and entrepreneurship between 1954 and 2004 The process is then replicated using an expanded corpus that includes all articles published in Management Science between 2005 and 2015. The discussion includes an analysis of the process and insights generated by using topic modeling. A replicable process for researchers and suggestions for practitioners are provided.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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