Assessing the Capacity of Pension Institutions to Build and Sustain Trust: A Multidimensional Conceptual Framework
Bibliographic record
Abstract
As policy makers have sought to reconfigure the public–private boundaries of their pension systems, trust has become an increasingly salient issue. At stake is the attainment of desired policy outcomes regarding retirement. By what criteria, then, should the capacity of pension institutions to build and sustain trust be assessed? This article emphasises the strategic importance of institutional design in the trust process. Building on Sztompka's seminal analysis of the institutional foundations of trust, and a substantial review of the literature and survey evidence regarding public confidence in pensions, we identify, justify and give indicative operational content to six trust benchmarks. This provides a conceptual foundation for future empirical research on the capacity of pension institutions to build and sustain trust.
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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 | 0.022 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".