Bibliographic record
Abstract
This thesis studies the concept of Low-carbon education and researches the background of the theory, then commits further analysis of the application of Low-carbon education theory in the fields of teaching. On the basis of this, the effectiveness and rationalization of application in the Low-carbon education theory in teaching were confirmed from many aspects. Among them, it is mainly analyzed both from the objective and subjective factors. These factors mainly exist in teaching methods. This thesis is developed from the following aspects: First discusses the relevant research on low-carbon education theory, define Low-carbon education and also illustrate function of Lower-carbon education in teaching; states the relationship between Low-carbon education and fuzzy theory, proves that there is internal consistency lies in Low-carbon education and fuzzy theory, these theories can be used in teaching; then discusses the investigation results under the situation of Low carbon education; at last discuss how to improve teaching methods under Low-carbon educational point of view, sums up the investigation results and puts forward the essence of teaching activity and the effective principles in teaching. It is necessary to advocate Low-carbon education in teaching in order that the complicated process can be effectively fulfilled. Teaching is not only a practice process based on text books. This can not be achieved by one single teaching method. Therefore we should advocate Low-carbon education theory in teaching in order to break the barrier in teaching and learning. We should take diversified forms of teaching, and explore the essential change so that the students can gain ability not only skills.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".