Ubiquitous Buddhism Learning Ecosystem Model
Bibliographic record
Abstract
The objective of this research is to develop and study the outcomes of developing the Ubiquitous Buddhism Learning Ecosystem for Proactive Buddhism Propagation for Digital Citizenship. The sample group used in the research consists of nine individuals selected through targeted sampling, comprising experts in the design and development of learning environment systems, as well as experts in Buddhism from various educational institutions at the tertiary level. The research findings indicate that 1) the Ubiquitous Buddhism Learning Ecosystem for Proactive Buddhism Propagation for Digital Citizenship consists of four components as 1) the Ubiquitous Buddhism Learning Ecosystem 2) Proactive Buddhism Propagation 3) Education Buddhism 4) Digital Citizenship, the results found that 1) In terms of the details of the Ubiquitous Buddhism Learning Ecosystem for Proactive Buddhism Propagation for Digital Citizenship, the overall picture is at the highest level. 2) In terms of the components of the Ubiquitous Buddhism Learning Ecosystem for Proactive Buddhism Propagation for Digital Citizenship are at the highest level overall. 3). In terms of Digital Citizenship knowledge of Buddhism, the overall level is high, and 4) Overall, the Ubiquitous Buddhism Learning Ecosystem for Proactive Buddhism Propagation for Digital Citizenship is at a high level accordingly. From the above research findings, it can be concluded that the Ubiquitous Buddhism Learning Ecosystem for Proactive Buddhism Propagation for Digital Citizenship can be further developed to enhance the effectiveness of future proactive Buddhist dissemination processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".