Alla för alla. Jämlikhet, korruption och socialt förtroende
Bibliographic record
Abstract
[All for all. Equality, corruption, and social trust] The importance of social trust has become widely accepted in the social sciences. In this article, Bo Rothstein and Eric M. Uslaner examine an overlooked key factor in shaping generalized trust, namely, equality. The omission of equality in the social capital literature is peculiar since the countries that score highest on social trust also rank highest on economic equality: the Nordic countries, the Netherlands, and Canada. The same countries have put a lot of effort in creating equality of opportunity, not least in regard to their policies for public education, labour market opportunities, and (more recently) gender equality. The policy implications that follow from the authors’ research are that the low levels of trust and social capital that plague many countries are caused by too little government action to reduce inequality. However, many countries with low levels of social trust may be stuck in what is known as a social trap with trust levels too low to sustain the universal policies that would reduce inequality. Publication history: A translation of a, by the authors, revised version of the article "All for All. Equality, Corruption, and Social Trust", originally published in World Politics , volume 58, number 1 2005 ( http://dx.doi.org/10.1353/wp.2006.0022 ). (Published 2 December 2015) Citation: Rothstein, Bo & Eric M. Uslaner (2015) "Alla för alla. Jämlikhet, korruption och socialt förtroende", in Arkiv. Tidskrift för samhällsanalys , issue 4, pp. 151–185. DOI: http://dx.doi.org/10.13068/2000-6217.4.5
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.280 | 0.152 |
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".