Bibliographic record
Abstract
The importance of social trust has become widely accepted in the social sciences. A number of explanations have been put forward for the stark variation in social trust among countries. Among these, participation in voluntary associations received most attention. Yet there is scant evidence that participation can lead to trust. In this article, the authors examine a variable that has not gotten the attention it deserves in the discussion about the sources of generalized trust, namely, equality. They conceptualize equality along two dimensions: economic equality and equality of opportunity. The omission of both these dimensions of equality in the social capital literature is peculiar for several reasons. First, it is obvious that the countries that score highest on social trust also rank highest on economic equality, namely, the Nordic countries, the Netherlands, and Canada. Second, these countries have put a lot of effort in creating equality of opportunity, not least in regard to their policies for public education, health care, labor market opportunities, and (more recently) gender equality. The argument for increasing social trust by reducing inequality has largely been ignored in the policy debates about social trust. Social capital research has to a large extent been used by several governments and policy organizations to send a message to people that the bad things in their society are caused by too little volunteering. The policy implications that follow from the authors' research is 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 and social capital may be stuck in what is known as asocial trap. The logic of such a situation is the following. Social trust will not increase because massive social inequality prevails, but the public policies that could remedy this situation cannot be established precisely because there is a genuine lack of trust. This lack of trust concerns both “other people” and the government institutions that are needed to implement universal policies.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".