Analysis of Environmental Risk Perceptions and Scores of Preservice Science Teachers in Terms of Some Variables
Bibliographic record
Abstract
This research aims to determine the environmental risk perceptions of preservice science teachers (PSTs) and compare their risk scores in relation to different variables. The research participant group consisted of PSTs (N = 205) from the Faculty of Education in the Department of Science Education at Bolu Abant İzzet Baysal University in Turkey. The environmental risk perception scale (ERPS) was used as a data collection tool and the environmental risk perception interview form (ERPIF) was used during the interviews. A survey model was used in the research. An enriched design in which quantitative and qualitative analyses were used together was included. Quantitative results from the research show “radiation,” “factory waste,” and “hazardous (chemical) waste,” as environmental problems that PSTs consider the riskiest. The least risky environmental problems were “overgrazing of animals in meadows and pastures,” “commercial fishing,” and “open mining.” According to the qualitative interview results, “air pollution” and “factory waste” were seen as the riskiest environmental problems, while “environmental waste” was considered the least risky environmental problem. In addition, while the females had a higher environmental risk perception than the males, there was a significant difference between the 3rd and 4th levels with 4th level PSTs favoring a higher environmental risk perception. There was no significant difference between the environmental risk perception scores of the PSTs depending on whether they took an environmental course or not; neither was there any significant difference issuing from the educational status of PSTs’ parents.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".