Bibliographic record
Abstract
Ethnic and other cultural diversity has become something of an ideological holy of holies in Western societies. However, in spite of idealistic shibboleths surrounding the concept, academic literature broadly supports the contention that ethnocultural diversity is negatively correlated with public goods provision, political stability, economic growth and the like. As such, a broad reexamination of diversity’s inherent desirability is necessary. This paper takes a two-pronged approach by conducting a critical review of relevant literature, and cross-referencing it with the case study of Nigeria. Whilst multicultural “settler societies” such as the USA or Canada boast a number of fundamental differences to postcolonial, “primordially” diverse societies such as Nigeria, the latter nonetheless offers a number of generalizable lessons which can be broadly applied to Western statecraft and policy making as well. Broadly speaking, an analysis of Nigeria provides considerable circumstantial evidence to support the academic consensus on ethnocultural diversity, and allows one to conceptually link big-picture, longitudinal studies with micro-level studies. At the same time, it provides considerable nuance to those broad conclusions, indicating that even though ethnocultural diversity is broadly correlated with lower levels of public goods provision, precise causes for this state of affairs tend to differ and diversity is far from the be-all, end-all of political instability, low levels of development and the like.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".