1 4 AS ONE OF THE DEPUTY DIRECTORS OF THE
Bibliographic record
Abstract
As in recent years, this was a large event; more than 400 delegates attended, and more than 200 papers were presented. We were host to a large number of African delegates, who made up a fifth of our regular attendees and more than a quarter of the participants of our plenary sessions. Several topics were covered in the research that was presented at the conference. Topics of our sessions included The marriage market, fertility and development; Agricultural value chains; and State capacity, political fragmentation, and elections. Individual papers addressed issues ranging from the role of oil prices in Ghanaian economic growth and Africa’s rising exposure to the Chinese economy. We were lucky to have Leonard Wantchekon as our keynote speaker this year. He presented his unpublished paper, Education and human capital externalities: Evidence from colonial Benin. He has gathered data on the pupils of some of the first mission schools in the former French colony, and traced the descendants of these individuals to the present day. Not only did education benefit the students who attended these schools, but one generation later it also raised the living standards of the descendants of first-generation individuals from the same communities who did not receive any education. We hosted two plenary sessions. The first, Mobile C O
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.225 | 0.125 |
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".