Bibliographic record
Abstract
Let us prefix the chapter with a story: Not too long ago, one of the authors of this chapter had a conversation with a colleague at their workplace about wasteful spending amongst some politicians when it comes to using public funds. During the conversation, the colleague spoke about a bridge that has been constructed to a road that leads to nowhere: “the government is building a bridge that is taking us to Timbuktu!” It was the first time Author Two has heard Timbuktu being used as a metaphor, so he asked the colleague what they knew about Timbuktu. The response was surprising. The colleague explained that Timbuktu is a fictional place, and it is used metaphorically to imply things that do not exist. Author Two had to explain to the colleague that Timbuktu was actually a city in Mali Empire (Africa) in the fifteenth century, and it was the hub for Islamic intellectualism with its famous Sankore University and other Madrasa attracting scholars from all over the world especially during the reign of Mansa Musa. The colleague was surprised by that information and wondered why they have not learned it in school. Such is the viciousness of Western education to take a notable accomplishment of Africa and make it a mockery in Europe and North America.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".