Perkembangan Infrastruktur Pasca Pemekaran Desa Teluk Paman Timur Kecamatan Kampar Kiri Kabupaten Kampar Provinsi Riau
Bibliographic record
Abstract
Desa Teluk Paman Timur mengalami pemekaran wilayah sehingga pembangunan infrastruk tur juga mengalami perkembangan dari segi jumlah dan jenisnya. Penelitian ini bertujuan untuk mengetahui perkembangan infrastruktur Desa Teluk Paman Timur pasca pemekaran serta faktor -faktor yang mempengaruhi perkembangan infrastruktur tersebut. Metode penelitian yang digunakan dengan pendekatan deduktif kualitatif. Metode ini digunakan untuk mengetahui perkembangan infrastruktur pada suatu desa dikarenakan pembangunan infrastruktur pada desa sudah mempunyai acuan atau peraturan pembangunan yang mengikat. Pengumpulan data dengan wawancara, observasi dan dokumentasi. Informasi diperoleh dari 10 informan. Analisis data secara kualitatif. Hasil penelitian menunjukkan bahwa pembangunan infrastruktur sudah menunjukkan adanya perkembangan setiap tahunnya. Namun, pembangunan infrastruktur tersebut terdapat kekurangan berupa bidang bidang yang mampu meningkatkan kesejahteraan masyarakat yaitu bidang lingkungan, kesehatan, dan pendidikan. Hal ini ditemukan penyebabnya antara lain faktor tatakelola pemerintah, partisipasi masyarakat, sumber daya finansial, kualitas sumber daya manusia dan masalah teknis lapangan.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.020 |
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".