Apraisal of Geography Teachers’ Knowledge on the Relevance of Secondary School Geography Curriculum Relating to Climate Change in Nigeria
Bibliographic record
Abstract
Evidence shows that climate change is experienced all over the globe. Climate change is defined as a change in the state of the climate that can be identified and measured by changes in the mean and/or variability of its properties Climate change can persist over a long time, usually over decades and much longer and leads to extremes of weather conditions such as temperature, wind, rainfall, and humidity. Geography curriculum is basically on climate and environment generally The main purpose of the study is to explore geography teacher’s knowledge, attitude and the practices relating to climate change in Nigeria. Questionnaire on the knowledge of climatic change among geography was used as the main instrument for data collection. It was designed to elicit information on the basis of research questions set for this study. A total number of 60 geography teachers were used as the sample size. Descriptive statistics such as simple percentages and frequency counts were used in the analysis of the research data collected. The findings from the research work show among others that the teachers communicate effective on the topics relate to climate change. It also reveals that most teachers have knowledge of the contents of geography curriculum. Geography teachers according to the findings indicates the readiness of the teachers to continue teaching the topics relating to climate change. It is therefore, recommends that topics on climate change be made compulsory for students at all level of education.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".