Roadmap Riset dan Publikasi; Menuju Keunggulan Kompetitif dan Komparatif IAIN Pontianak Berbasis Kebudayaan Islam Borneo
Bibliographic record
Abstract
Unsur-unsur yang harus ada dalam penyusunan Roadmap riset dan publikasi adalah; a) Rencana Strategis Perguruan Tinggi, b) Rencana Operasional Perguruan Tinggi , c) dokumen Penelitian 4 tahun terakhir, d) data kepakaran dosen berdasarkan keahlian/bidang ilmu dan sertifikasi, e) analisis SWOT, f) visi misi fakultas, jurusan dan unit kerja, g) hasil evaluasi diri, h). Roadmap penelitian invidivu dosen, i) keahlian/bidang ilmu dosen, j) kesesuaian dengan mata kuliah. Pembuatan Roadmap Riset dan Publikasi harus melibatkan seluruh civitas akademika yang perwakilan dari unit yang berkaitan dengan profesionalisme dosen, yaitu Rektor, wakil rektor I, II dan III, fakultas, jurusan, konsorsium dosen. Tracking Kepakaran dosen diupayakan melalui diwajibkannya dosen IAIN Pontianak untuk membuat Roadmap penelitian dan publikasi secara individu, penyesuaian penelitian dan publikasi dengan kepakaran, penyesuaian penelitian dan publikasi dengan visi misi jurusan yang menjadi homebase dosen/peneliti.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.014 |
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".