Prospects of Mind Maps for Better Visualization of Infrastructure Literature
Bibliographic record
Abstract
Literature analysis is important for the identification of the state of existing knowledge and prevailing research gaps. Effective literature analysis, however, is a lengthy process and requires a large effort to consider the information from different viewpoints and to identify areas of cross benefits. This paper represents an approach to summarize the information related to the construction and infrastructure domains by using a category of visual tools referred to as mind maps. First, the capabilities of various knowledge mapping tools for graphically representing the hierarchical concepts (keywords) of a given domain of knowledge are discussed, and example mind maps are developed for the infrastructure asset management domain. Enhancements to mind maps are then proposed on the basis of an extensive literature analysis to visually show numerical scores of various publications associated with the concepts in the mind map. This facilitates the identification of the highly relevant and the most useful knowledge in the literature. Suggestions are then presented for the use of mind maps by publishers of large literature databases to facilitate better analysis and the visual access and retrieval of information from the literature.
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.
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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".