Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
African political leaders have a tendency to favor members of their own ethnic group. Yet for all other ethnic groups in a country, it is unclear whether having a similar ethnicity to the leader is beneficial. To shed light on this issue, I use a continuous measure of linguistic similarity to quantify the ethnic similarity of a leader to all ethnic groups in a country. Combined with panel data on 163 ethnic groups partitioned across 35 sub-Saharan countries, I use within-group time variation in similarity that results from a partitioned group's concurrent exposure to multiple national leaders. Findings show that ethnic favoritism is more widespread than previously believed: in addition to evidence of coethnic favoritism, I document evidence of non-coethnic favoritism that typically goes undetected in the absence of a continuous measure of similarity. I also find that patronage tends to be targeted toward ethnic regions rather than individuals of a particular ethnic group. I relate these results to the literature on coalition building and provide evidence that ethnicity is one of the guiding principles behind high-level government appointments. (JEL D72, J15, O15, O17, Z13)
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it