Approach to Identify Topics in a Collection of TIM Review Articles and their Changes Over Time
Bibliographic record
Abstract
Topic modeling can help better understand the content of large collections of text.The objective of the research is to develop an approach to identify latent topics in the Technology Innovation Management Review journal (TIM Review) and how topics have evolved using the LDA (Latent Dirichlet Allocation) and DTM (Dynamic Topic Model) algorithms.We applied the two approaches to a collection of TIM Review articles published between 2007 and 2017.We also examined extracted topics, most associated articles, topic labeling, topic/theme trends, and word trends produced by both approaches and identified the value of each approach.According to the results obtained, we identified 47 topics and categorized them into ten themes: open source, entrepreneurship, innovations, living labs, social technology innovation, growth, co-creation, cybersecurity, research, and ecosystem.While some topic trends became prominent over time, others disappeared.The distribution of the articles across topics in the LDA approach has been made more decisively so that of 597 articles, 503 most associated articles were identified, while this number is 299 articles in DTM.Furthermore, we discussed weaknesses and strengths of the algorithms to compare the performance of the two approaches based on defined criteria.We conclude that DTM provides more accurate word and topic trends over time, although it requires time slice settings and has a longer run-time compared to LDA.Finally, we document a set the steps of a process to carry out topic modeling analysis.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.034 | 0.023 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".