Bibliographic record
Abstract
The aim of this research was to determine the environmental awareness of teacher candidates. According to this aimthe research was carried out with a total of 532 students randomly selected from all departments of Trakya UniversityFaculty of Education and Pedagogical Formation program students during the fall semester of 2016-2017.“Environmental Awareness Scale" prepared by the researcher was used as data collection tool. The scale consists of71 items with a rating of 4. For each sub-scale, the internal consistency was determined by calculating the item-totalcorrelation coefficient and item-remainder correlation coefficient. In the same way, t-test between the upper quartileand the lower quartile was applied to detect the discrimination power of the items. For the scale and sub-scales, thereliability was determined by calculating the Cronbach and Rulon coefficients. It was determined that the scaleconsisting of 3 factors was valid, reliable and usable after statistical procedures. High scores in all items and factorsindicate positive environmental awareness.A questionnaire consisting of 19 questions prepared by the researcher was used to collect data about the independentvariables of the research. The questionnaire contains questions about nature love as well as demographiccharacteristics such as gender and age. The statistical analysis of data featured t test, variance analysis and LSDmethods for Post hoc analysis to determine the source of variation. In this research, generally it was found that theenvironmental awareness of the candidate teachers was very high.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".