Bibliographic record
Abstract
This doctoral dissertation studies the significance of school maps in education in detail and complements findings by experts from Slovenia and abroad with new insights. Based on the studies conducted, textbooks remain the predominant method for presenting cartographic material in the educational system, and therefore the main focus is on the maps used in textbooks. \nThe concluding thesis proceeds from an analysis of Slovenian curricula and their comparison with selected curricula in other European countries, Canada, and Australia. The findings are also based on an extensive analysis of the cartographic knowledge of Slovenian primary-school and secondary-school students, teachers’ preferences, and the experience of the editors that incorporate cartographic material into textbooks. These analyses were carried out using a survey and interviews. The results showed that, compared to the curricula in other countries, the Slovenian curricula provide extensive and thorough cartographic material, especially from the fourth grade onwards. Nonetheless, the students show gaps in certain segments of cartographic knowledge of Slovenia. \nIn order to study the causes for this, the entire cartographic communication system was examined—from the cartographers that encode the messages, to cognitive maps, which are the result of users’ mental decoding of messages provided by the map. The study adds to the cartographic design principles that will serve as a starting point for preparing the best possible school maps for teaching in the future and for further development of school cartography.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".