Bibliographic record
Abstract
Making a unity of color components in every racial, ethnic, and ethnic, cultural and other differences in an area with the purpose of da’wa, that is the mosaic da’wa that a da'i will face and live on his journey especially in Indonesia. Every life form deserves live and develop, including ideas, perspectives, policies, attitudes and actions, by the people of a country towards something that is present and into the territory of certain regional communities. Multiculturalism began to be made an official policy in English-speaking countries (English-speaking countries), which began in Canada in 1971. In accommodating diverse community lives, the government then adopted the concept of cultural mosaics. This concept illustrates the diversity of ethnic groups living side by side in Canada where people can adapt themselves to differences in cultural ethnicity and the uniqueness of each culture. This gives different contrast spark plug to Canada. Cultural mosaics consist of three main categories: (a) demographic, (b) geographical, and (c) associative of cultural mosaics as in the times of Sunan Kaliogo in Indonesia at the time of da’wa at that time. Demographic variables related to race and ethnicity are most closely related to culture in general. Geographical variables refer to the physical features of an area. Associative Variables, forms and ways of social interaction that can enhance solidarity relations among humans. The collaborative effort to achieve a common goal that was applied by Wali Songo, namely the missionary goals and welfare of the Nusantara community in the 15th century 674 AD was equivalent to the mid 1470s.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.186 | 0.193 |
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".