Analisa Pemangku Kepentingan Kebijakan Pengelolaan dan Pengembangan Sumber Daya Manusia (SDM) Kehutanan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Management and development of forestry human resources is very important on attaining the objectives of forestry development towards sustainable forest management (SFM) and prosperous society. Lack of proper policy on forestry human resources management and development may degrade the quality of forest governance. Good understanding of the dynamics of power, interests, knowledge, and networks of stakeholders affecting the structure and performance of forestry human resources and it can be done by using an institutional approach to stakeholder analysis framework. This study aimed to determine the parties' interests and influences in policy-making on forestry human resource management and development. The study was conducted using snowball sampling method both in internal and external of the Ministry of Environment and Forestry. It is found that sixteen stakeholders involved in the policy-making on forestry management and human resources development, which can be divided into the groups of: subjects, key players, context setters, and crowds. The existing relationships are cooperation, complementary and conflict. It needs good understanding, clear rules and strong leadership in order to increase the optimal role of stakeholders and to determine human resource management policies and development.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it