Bibliographic record
Abstract
About a decade ago, I founded the Asta-Ja Framework which identifies Eight Ja—the Nepali letter “Ja,”—meaning Jal (water), Jamin (land), Jungle (forest), Jadibuti (medicinal and aromatic plants), Janashakti (manpower), Janawar (animlas), Jarajuri (crop plants), and Jalabayu (climate), and proposes their sustainable conservation, development, and utilization for fast-paced socio-economic transformation of Nepal. It is a scientific, holistic, systematic, self-reliance, and multidisciplinary grassroots-based framework for conservation, development and utilization of Asta-Ja resources. For its practical application, I proposed eight principles: 1) community awareness, 2) policy decision making, 3) community capacity-building, 4) interrelationships and linkages, 5) comprehensive assessment, 6) sustainable technologies and practices, 7) institutions, trade and governance, and 8) sustainable community development and socio-economic transformation. The first decade of its implementation in Nepal characterized with a vigorous community outreach, strong membership drive, sound policy advocacy, heavy engagement of high-level government officials and dignitaries, community capacity-building, disaster relief works, and cutting-edge research and development. Future direction for its effective implementation include: 1) institutional strengthening, 2) coordination with governmental agencies and other stakeholders in planning and management of Asta-Ja resources, 3) expedited research and development on Asta-Ja resources, 5) formation of Asta-Ja Consortium, 6) development of a comprehensive Asta-Ja Data Portal, and 7) the establishment of Asta-Ja Think Tank.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".