Advokasi Isu Konservasi Arsitektur dan Kota melalui Seri Artikel Ilmiah Populer pada Situs Media Daring
Bibliographic record
Abstract
The low level of public understanding about historical and architectural values of historical areas in both urban and rural areas often leads to neglect or even destruction of cultural heritage. This community service activity aims to increase public awareness of the importance of architectural and urban conservation through a service learning approach in the form of advocacy based on popular scientific writing in online media. To this end, the implementing team compiled and published four popular scientific articles that discuss conservation issues in communicative language and are tailored to the characteristics of general readers. The implementation method consists of three main stages: preparation (collection of materials, writing training), preparation and correction of articles, and publication and dissemination through various digital media. The results of the activity show that this strategy is able to reach a wider audience, encourage public discussion, and raise new awareness of the importance of preserving urban architecture. This activity also succeeded in positioning academic knowledge as an integral part of the media-based social advocacy movement. In the future, a similar approach is recommended to be expanded in scale with policy support and cross-sector collaboration.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.022 |
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".