Integration versus segregation: Newspaper diversity and museum formation in US local communities 1872–1976
Bibliographic record
Abstract
Motivated by general theoretical ideas about the societal consequences of diversity among organizations, I examine how the diversity of organizational forms in a local community shapes the formation of its museums. I argue that the diversity of organizations that cut cross major community segregation lines helps to integrate a community and consequently enhances its ability to act collectively, in this instance to create a museum to serve the community. In contrast, the diversity of organizations that are confined to the major segregation lines widens community cleavage and decreases the community’s ability to establish a museum collectively. Empirically, I investigate how diversity in the local press affected the formation of museums in American counties from 1872 to 1976. The findings provide empirical support for my theory. I find that the diversity of general appeal newspapers has a positive relationship with the formation of museums, implying the integrating effects of general appeal newspapers that cut across boundaries of race and ethnicity, the dominant segregation lines in US communities. In contrast, there is a negative relationship between ethnic newspaper diversity and museum formation, showing the segregating effects of ethnic newspapers by race and ethnicity. I conclude the study with a discussion of its implications for a general research program on how organizational diversity shapes social interaction patterns in a community, and consequently influences the community’s civic engagement for public good.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".