Pola Bussines To Bussines Dalam Pengadaan Tanah Bagi Pembangunan Bandar Udara Kediri
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
The issuance of Presidential Regulation Number 109 of 2020 on the Third Amendment to Presidential Regulation Number 3 of 2016 on Accelerating the Implementation of National Strategic Projects aims to increase infrastructure development in Indonesia. One of the realizations is through government cooperation with business entities. Airports are infrastructure that requires land in its development through land acquisition. This research uses a qualitative method with a descriptive analysis approach. The data collection techniques were observation, interview, and document study. The results of the research are the process and progress of the implementation of land acquisition for the construction of Kediri Airport which is carried out through the business to business (B2B) pattern and the stage pattern. Problems in land acquisition include the existence of parties other than the land acquisition team involved in it, lack of understanding of the land acquisition mechanism, no coordination from the party that requires land with the Ministry of ATR / BPN, and people who reject the value of compensation both when doing the B2B pattern and the pattern of land acquisition stages for the public interest. So that data verification, community and village apparatus guidance, coordination related to land acquisition for the construction of Kediri Airport with the Kediri District Land Office, and settlement through location determination to resolve problems in B2B for people who reject the value of compensation and consignment for people who reject compensation during the pattern stage.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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