Occupational Pension Funds (IORPs) & Sustainability: What does the Prudent Person Principle say?
Bibliographic record
Abstract
The European Union encourages individuals to save in private and occupational pension funds to complement their state saving-plans. Throughout their lives, employers directly sponsor occupational retirement saving plans, so individual employees may top up their future pensions. While the European Union clearly supports the formation and cross-border participation in these financial vehicles by adopting EU regulatory framework, the EU has also decided to determine a common investment decision standard to be used in all Member States, called the Prudent Person Principle. According to this principle, the fund - the future retirement for many - shall be managed with care, the skill of an expert, prudence and due diligence. Under this principle, the pension fund’s governing body is given a broad authority to invest the pension assets in a prudent fashion in light of the particular investment plan of a fund. At the same time, the EU is also moving towards more Responsible Investment and inclusion of the ESG-principles (Environment, Social and Governance). The question we aim to answer in this paper is how these two principles co-exist and whether, due to the new Directive adopted by the occupational pension funds in 2016, all funds are obliged to make only responsible, environmentally and socially beneficial investments.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".