Student Science Teachers’ Ideas about the Degradation of Ecosystems
Bibliographic record
Abstract
The aim of this research is to investigate student science teachers’ opinions about the causes of degradation of ecosystems and the effects of such degradations on the environment. This research focuses on the following questions: What kind of descriptions do student science teachers ascribe to the reasons of degradation in ecosystems? What are the effects of ecosystem degradations on the environment? What are the misconceptions in relation to degradations in ecosystems? A total of 130 participating students, who were studying to become science teachers at Faculty of Education of Necmettin Erbakan University in Turkey, participated in this study. To reveal the participating students’ opinions about the reasons for degradations in ecosystems and their effects on the environment, they were asked to answer two open questions: (1) What are the reasons for degradations in ecosystems? (2) What are the effects of degradations in ecosystems on the environment? The participants were asked to answer these two questions. Data obtained from the questions were analyzed and the frequencies of the answers were classified in different categories. Moreover, these included some misconceptions such as ‘the greenhouse effect can lead to skin cancer’ and ‘ozone layer depletion leads to global warming’. The findings are compared with related literature and suggestions are presented.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".