The Concept of Justice in the History of Iran's Islamic Revolution
Bibliographic record
Abstract
The concern of the emergence of an equitable life has always been with mankind throughout the history. Therefore, different approaches and how to implement the concept of justice in society have raised, especially among Islamic and Western scholars. One of the scholars who have studied the issue of justice in Islam is Imam Khomeini. A question that arises is: What is the interpretation of justice in the thoughts of Imam Khomeini? This paper will describe the characteristics of justice through the thoughts of Imam Khomeini. Imam Khomeini, is one of the greatest thinkers of the Islam’s world who has an Islamic interpretation of the meaning of justice and how it is realized in society, with the influence of Islamic thoughts in Qur’an and the Prophetic and Alawi’s behaviors or manners. He knows justice as a natural thing in human nature that the great God puts it in human’s nature. Imam Khomeini describes the indicators of justice in a society in this way: Respect to the talents of people in the society, being moderate in the affairs, The priority of social Interests on the individual interests; Establishment of truth and justice in society and having a fair view to the human and the alleviation of poverty and exclusion from society.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".