Bibliographic record
Abstract
The Little Leopard that Became a Big Leopard (rendered as bé gɔ̧ nɛ kifa gásá gɔ̧ in modern Gbaya orthography) is a mimeographed pamphlet that surfaced in 2023 among Smathers Libraries’ uncatalogued materials purchased from African historian Donald Abraham. It included none of the information we needed to understand what it is, so Principal Cataloger William (Hank) Young brought it to my desk. I called for help from my linguist contacts, who narrowed down the possibilities. Eventually, I contacted professional linguist Dr. Phillip Noss, a Gbaya specialist who, surprisingly, lives right here in Gainesville! He proved to be the key to understanding this mystery pamphlet. Noss confirmed the pamphlet was written in the 1940s by linguists at the Sudan Mission in Meiganga, Cameroon. There and in nearby Central African Republic (CAR), Canadian linguist Madel Nostbakken served as Book Editor with overall responsibility for Gbaya language work until her retirement in 1975. She may be the author. Our pamphlet bé gɔ̧ nɛ kifa gásá gɔ̧ is now cataloged for Rare Books along with about twenty titles donated by Noss. We may now own the largest collection of Gbaya texts and teaching resources documented in WorldCat, but the Little Leopard is my favorite!
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".