Bibliographic record
Abstract
The plane collided with the runway, and after a few bumps became one with the earth.It slowed and coasted the rest of the way.The runway was surrounded by safari, dotted with acacia trees.Wandering amidst the grassy plane were animals of all sorts: giraffes mingled with tigers, and lions with zebras.As we stepped out of the plane, the animals gathered in a circle and began to sing the "Circle of Life."At least that is how I imagined it when I heard that I would be going to Addis Ababa, Ethiopia, to work with kids who live on the street.My knowledge of Africa came from the Lion King and documentaries on isolated tribal groups.I wondered how I would share the gospel working with what I imagined to be impossible language and cultural barriers.But, every preconceived notion I had flew out the window when the wheels collided with the runway, and the city of Addis Ababa, not a herd of animals, greeted us.As we grabbed our bags and headed towards our van, I was taken aback with what awaited me on the other side of the airport doors.The second we stepped out, there was a crowd of people competing to earn a little money and carry our bags.There were hands reaching to grab our stuff because once they touched it, we had to pay for their services.With an iron-grip on our suitcases, we pushed through the crowds and made it to our driver, Tasfaye.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.143 | 0.035 |
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".