Bibliographic record
Abstract
This study aims to examine the thoughts of fourth-year pre-service science teachers about the nature of science. For this purpose, the study preferred the qualitative research method and phenomenology design. Participants consisted of nine pre-service science teachers. Participants wrote a self-evaluation report after the nature of the science course. I collected data with this report and focus-group interview. I did the data analysis using content analysis. The current study discusses validity and reliability. As a result of the analysis, pre-service science teachers stated that they learned the nature of science by adopting the constructivist philosophy. Therefore, they described that they will use the constructivist scientific language when they become teachers in the future. They argued that students should learn how scientific knowledge changes and develops. In addition, pre-service science teachers emphasized that teachers should direct their students’ learning processes according to the constructivist philosophy considering scientists’ culture, beliefs, and prior knowledge. They stated that students should understand the socio-cultural impact on the learning process. They also mentioned that students should use their imagination and creativity so that they can learn scientific information meaningfully. Based on these results, I suggested that teaching based on positivist philosophy would not remove the obstacles to students’ learning science.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".