Bibliographic record
Abstract
My Arab friends owed me an apology.It was the late 1980s, and I was pursuing graduate studies at the University of Alberta in Canada.There, for the first time, I met Muslim Arabs, who happened to be students.No Arab had ever been seen in Rogbap, my village in Sierra Leone.In fact, the words "Islam" or "Muslim" were hardly used at Rogbap, just olaneh, believer.To be a Muslim was like having a head on one's shoulders, just a simple fact of life.But after I left my village I met many Arabs and Muslims-in books of history, literature, and literary criticism.In Canada I met Arabs for the first time.And they owed me an explanation.Their ancestors, horsemen with curved swords, had stormed Africa, my homeland, ravaged the land, enslaved some of the people, and forcefully converted many others.Their crimes against Africa cried out for reparation.Some of my undergraduate professors of literature convinced me of the great violence and intolerance that accompanied the advent of Islam in sub-Saharan Africa.Two Thousand Seasons by Armah and Bound to Violence by Ouologuem featured prominently in our literature curriculum.Also, the works of Sembène Ousmane show what Islam is: a fatalistic religion, an opiate of the people, the explanation for the backwardness of African countries with a majority Muslim population.As for those novels with no violent content, it was evidently because they did not have an Islamic theme.The Radiance of the King by Camara Laye was a Christian allegory.It did not matter that Camara Laye was, as I learned later, from a traditional Muslim background.My Arab friends did not seek to defend the record of the Arabs in Africa.Before meeting me, most of them did not even know of a country called Sierra Leone.Their silence challenged the image of Arabs and Muslims that I had in my head.I was forced to revisit that image, its source, and its enduring influence on African letters and thought.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.595 | 0.420 |
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".