ETIKA ILMIAH ISLAM SEBAGAI WUJUD TOLERANSI (Analisis Pemikiran Taha Jabir al-Alwani dalam Kitab Adab al-Ikhtilafi fi al Islami)
Bibliographic record
Abstract
The rise of change and the behavior of society today change the distortion or differences in factors, news, and issues those are freely scattered in the mass media are no longer heard by the Indonesian people, so it becomes the people's consumption that is swallowed raw so that it starts to fix it. When people take news and information about and direct then many questions arise, it is appropriate, but the problem is all the news that comes from the mass media used to criticize and disbelieve others, this causes a variety of differences that result in the decline of humans to be able to support each other and make a difference as a mercy for all nature. All kinds of actions that cause conflict and friction between human beings are triggered by negative ideological reasoning that challenges its truth. Because reason and ideology that are formed do not show a more wise view and behavior in responding to differences. Therefore, the author wants to discuss more deeply about ethical knowledge and scientific knowledge of scientific ethics capable of realizing a tolerant attitude and understanding perspective. Of course, this author uses the Content Analysis method as the main reference in examining the views of Taha Jabir al-Alwani about attitudes and actions relating to science using interpretive-descriptive methods. Then the results obtained from scientific ethics are resolved (1). Self-awareness consists of magical, naive, critical, and transformative awareness,(2). Social awareness consists of mutual support, compilation, understanding, (3). Revolution awareness and (4). Divine Awareness is a difficult challenge to avoid because of hostility, strife, strife, termination of akin relations which is opposed by Allah.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".