Determining the Cognitive Structures of Geography Teacher Candidates on “Earthquake”
Bibliographic record
Abstract
The objective of this study is to determine the cognitive structures of the students of geography teaching department by identifying their conceptual frameworks about the concept of earthquake. A case study design from qualitative research approaches was used in this research. Sample group of the study constitutes 155 students from the Department of Geography Teaching who took the course of natural disasters. Free Word Association Test was used to collect the data. The data were evaluated according to the content analysis, categories were formed according to the results of this evaluation and frequencies and percentages of the response words in each category were calculated. A total of 9 categories were created according to their semantic associations. Some of them are; “concepts about earthquake, damages of the earthquake, a category of defining earthquake, types and causes of earthquakes, landforms caused by earthquakes and other effects” and they form the dominant categories. The frequencies and percentages of some categories are low such as; “The things that earthquake makes feel, regions where earthquakes happened before or there is a possibility to happen, people, institutions and organizations about earthquakes, helping to the victims of the earthquakes, factors affecting the safety of life and property in the earthquake”. In addition, this study revealed that students have some alternative concepts about earthquake.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".