Bibliographic record
Abstract
The realities of Iranian life are far more harrowing than most people imagine from the outside. Ganji paints a portrait of duplicitous clerics arbitrarily arresting, torturing, mutilating, and executing citizens, all in the name of Islamic Justice. A system of apartheid has been instituted against women. While 60% of the population lives below the poverty line, the mullah regime has hoarded billions of dollars in accounts and properties in Europe, Canada, and Japan. Roughly 70% of the population is under 30 years of age and opposes the regime. In the year of 2001 alone, 220,000 people—mostly educated youth—left the country in search of better lives. Ganji stresses that the best defense against terrorism is offense, and that the United States can and must establish a proactive policy of helping Iranians struggling for the freedom of Iran, in and out of the country. Western policies toward the Iranian mullah regime have thus far been reactionary rather than proactive. The regime in Iran has been an incubator of international terrorism, aiding and abetting international terrorist groups in and out of the Middle East. The author argues that now is the time for the United States to substitute rhetoric with action in policies toward the ruling clerics in Iran.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".