Federation to Maspul Ethnicity: A Constructionist and Historical Explanation
Bibliographic record
Abstract
Objective: This research aims to explain the history of the formation of Maspul ethnicity, to describe the characteristics of cultural diversity, and to explain the dynamics and reproduction of its contemporary cultural uniqueness. Theoritical framework: Through the application of the perspectives of constructionism, historical particularism and specific ethnic historical studies. Method: With reference to the research objectives, this study uses a qualitative ethnography method to collect present and past socio-cultural data which is the root and basis for the development of Maspul ethnicity's life. Result: The research founds at least three findings. First, the contemporary Maspul ethnicity and the three sub-ethnicities of Enrekang, Duri, and Maiwa are the transformations of the Maspul Federation and the kingdoms in the mountain. Second, the diversity of the contemporary Maspul culture which is distributed in the three Maspul sub-ethnicities originates the old cultural diversity that characterizes the Maspul Federation which was an ethnic unity at that time. The old cultural elements that still survive from each ethnic group are language (in terms and dialects), traditional institutions, traditional ceremonies, art, games, traditional food, local knowledge, folklore, and mythology. Thrid, Since the last few decades most of Maspul's cultural elements have changed although several others being preserved with attractive new forms of packaging. Conclusion: the Maspul Federation which transformed into Enrekang Regency is not only seen as a phenomenon of political history, but it is also a historical phenomenon of ethnic and cultural history.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".