Bibliographic record
Abstract
Q. Sir, I would like to know something about messengers and interpreters like Adamu Jakada. A. Adamu Jakada was the messenger between Emir Abbas and the Europeans. Some of the messengers and interpreters were employed by the emir, and they were royal slaves. Whenever they did something wrong they were replaced by others. Adamu was a slave of the emir. Q. Where and when was he born? A. He was born in Kano, and he came from the family of slaves. Q. Is there any story about him? A. All we know is that he was chosen by the District Officer (D.O.) He would take messages from the emir to the white men and return with the white man's reply to the emir. Q. Were messengers and interpreters powerful? A. Yes, indeed. Q. Were they nice people? A. It is when they became powerful that issues of misunderstanding occurred. You know when somebody becomes powerful the person would demonstrate good and bad qualities. Some of the messengers and interpreters were like that. Q. Were they wealthy? A. They were slaves of the emir. Everything that they owned, they took them from the servants of the emir. Also, they were paid by government. Q. What did people think of their work? A. People respected and feared them because of their closeness to the emir and the Europeans. Q. Did they speak or write in English? A. Before they learnt English they used to work as servants to the Europeans. So they learned English from them.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".