Bibliographic record
Abstract
The increasing use of targeted social investments has led to relevant research interest in the transaction costs of social efforts. However, the majority of the research is characterised by the following two challenges: first, the analyses are often limited to public sources of revenue and, therefore, exclude private sources; and second, the transaction costs are measured based on self-declared information about administrative costs. The article contributes to the field of research in two ways. First, the contribution is made through an analytical model that brings together private and public revenue streams in a single model, providing a unique opportunity to compare the transaction costs from these two sources. Second, in this article, transaction costs are measured based on the actual development in the number of administrative academic full-time equivalents (FTEs) in the organisations. The latter attribute also achieves a better link to the theory in the field, which precisely focuses on administrative employees. The article derived data from a longitudinal dataset for 2012‒17 with the accounts for revenue in the nationwide voluntary social organisations and register data from Statistics Denmark on the education and working hours of employees in organisations. The article finds that targeted project funds (that is, earmarked funding) from private sources have significantly higher transaction costs than government project funds and general public operating grants. Smaller organisations were also shown to generally have higher costs when striving to secure funding than larger organisations with economies of scale.
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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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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 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".