Bibliographic record
Abstract
Our man is the consular officer at the Permanent Mission of the Republic of Zimbabwe to the United Nations office in Geneva, which also serves as the country’s embassy to Switzerland. At fifty-five and in his first foreign posting, he is a latecomer to the Internet and all its glories. “Baba, get e-mail,” his children said back in Harare. There was no need, he always said. Too expensive, too set in his ways. In Geneva, the connection comes with his telephone line. Night after night finds him enmeshed in the World Wide Web, scrolling through e-mails spun in places he has never been, e-mails that are woven into his life and leave him blinking before his computer screen. He types slowly, with two fingers, his tongue between his teeth. “Like a policeman typing a report on a burglary,” his wife teases him, “at the Charge Office in Harare.” See how easy communicating becomes, says his daughter, Susan, in England. Don’t forget to send the installment for the next semester. She follows the sentence with several bouncing bald, yellow, bodiless cartoon heads that open their mouths in toothless smiles as they wink at him. Baba, I need money, is the echo from his son, Robert, in Canada. Improve your credit rating, says Frederick Turk.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.355 | 0.195 |
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".