Bibliographic record
Abstract
America is a heterogeneous country, open at least potentially to people of all backgrounds willing to accept her defining principles and to assimilate to the culture that embodies them. Since the 1960s, however, mass non-Western immigration, coupled with the advance of multiculturalism and the “celebration” of “diversity,” has weakened the understanding and transmittal of those unifying principles. And then came September 11, 2001. As Hillel Fradkin, director of the Center on Islam, Democracy and the Future of the Muslim World at the Hudson Institute, remarks, “radical Islam poses an unusually severe problem for multiculturalism,” in that it “is selfconsciously hostile to liberal democracy, while at the same time demanding a place in American society. That’s an obvious and difficult contradiction.” Even to speak freely about Islam and to find the correct vocabulary to do so has been difficult. President Obama, following President Bush, describes Islam as a “religion of peace” that has been “hijacked” or distorted by a few violent extremists. Many use the terms “Islamism” and “Islamist” to distinguish the radical element from the more moderate aspects of the faith. But the Council on American-Islamic Relations (CAIR), a Muslim advocacy Acad. Quest. (2011) 24:4–10 DOI 10.1007/s12129-011-9213-3
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.040 | 0.005 |
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".