A Comparative Study of Saraiki Animal Tales with the American Animal Tales
Bibliographic record
Abstract
Recently, the researchers and scholars have developed a tradition of reviving past and fostering nationalism among the speakers through their past/history. Saraiki civilization has also its both tangible and intangible assets like other oldest rural civilizations of the world. There at international level, a lot of work has been done in the field of folktales so far as their classification according to their types structures and functions. In Pakistan, no significant work has been done. The study is aimed to compare the Saraiki animal tales with the American animal tales through the American model developed by Uther in 2004 popularly known as Aarne-Thompson-Uther model. The current study is the comparative structural analysis of the Saraiki folktales. The Saraiki folktales were collected through participant observation, observation and interviews. The study was conducted in the rural areas of Southern Punjab, Pakistan. The multistage approach was applied to collect the folktales from the research area on the principle of probability sampling through the informants from the study area. The researcher for this purpose used the purposive sampling technique to select the informants of his study Saraiki folktales. The researcher selected 09 animal tales were compared to the plots recorded in an international American Arne-Thompson-Uther (2004) model generally known as the ATU and traced similarities of plots between the Saraiki folktales and internationally recorded American animal tales. The Saraiki oral tradition may be collected and classified for the preservation of cultural heritage and for further research in this field.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".