Using the method of mind mapping in the Institute of Education and Communication of the Czech University of Life Sciences in Prague
Bibliographic record
Abstract
The need to find a method of a logical and systematic arrangement of information arises from the long-term experience of the educational staff of the Institute of Education and Communication (“IEC”). A certain group of students, practice training teachers in particular, prepare for consultations by learning the whole passages of the respective textbooks or electronic support by heart. The knowledge acquired in this way is not very long-lasting and in practice it is almost inapplicable. There is no context with other topics, no systematic knowledge of basic concepts and in the subject matter learned in this way, the students are not able to trace for instance the hierarchy of the concepts used. The method that may help solve this problem is mind mapping. Mind maps represent a specific way of taking notes allowing to capture clearly the structure of the problem to be solved both in terms of individual elements of the structure and their mutual links. The author of the method of mind mapping is a Canadian psychologist Tony Buzan. In his statement: “A mind map is the most powerful organizational tool of our brain”, he expresses the findings of his long-term study of the function of the brain. In his view, mind maps respect the natural qualities of our mind.
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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 | 0.005 | 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 | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".