The image of the wolf in prose works and dialogical training
Bibliographic record
Abstract
This article compares the image of a wolf in native and foreign prose works and discusses the importance of dialogical teaching. The educational value of works of art depicting animals, including wolves, which contribute to the formation of man as an individual, is analyzed and discussed in foreign prose. The world-famous J.London, who paid special attention to the human problem by describing the relationship between man and wolf. A comparative analysis of the large-scale work of London "White Fang" and the story "Kokserek" by M. Auezov. Also, in response to questions from students, J.London's "Wolf", Ernest Seton-Thompson's "Lobo", "Winnipeg Wolf", Kazakh writer D.Ramazan's "Kokzhal" stories are also compared and the internal psychology of wolves raised in human hands and wolves raised in their environment is systematically analyzed. The answers to the students' questions about the attitude of man to animals are discussed in the works. The lesson takes into account the views of well-known researchers on dialogical learning, covering the theoretical and practical significance of the use of the method of research conversation, a type of dialogic conversation. In this study, the author's ideas in these fictional prose and the reader's own views on the inner psychology of the wolf were raised. In a comparison of foreign and domestic classical prose, in which the image of the wolf is embodied, along with the main idea of the writers, the readers' conclusions were also taken into account. When analyzing the internal psychology of the wolf, examples of tasks that are given to each group in the class were given. One of them said, "How could I finish a work if I were a writer..." you can work in this direction. When answering the questions asked, it was shown that the students are not only fully familiar with the text but also enter into a dialogue with critical comments. The importance of dialogical teaching, which increases the interest of students in lessons, is evidenced by the works of such researchers as J. Aimautov, D. Barnes, N. Mercer. In all the works about the wolf, strictly taking into account the psychology of the laws of nature, it is clear that the attitude of man to animals is revealed from different sides. With a comparative analysis of the differences in the wolf's relationship with people, their fate, their psychology in the works, I was convinced that dialogical conversation is quite acceptable. One of the types of dialogic conversation is the effectiveness of the method of research conversation, i.e. the ability of students to stay in their thoughts, to argue their opinion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".