Decoding Pension Funds: Sustainability Indicators for Annual Report Analysis
Bibliographic record
Abstract
Pension funds’ growth highlights the need to emphasize fiduciary duty and investment sustainability, considering the current and future participants’ interests (priority stakeholders) and systemic risk reduction (environmental, social, economic, and governance effects). Therefore, this study builds sustainability indicators based on the interests of pension fund stakeholders. The methodology comprised five stages: the first consisted of analyzing Annual Information Reports to create a preliminary list of indicators; the second involved examining specific legislation on pension fund disclosure and identifying mandatory information; the third involved submitting the updated list to experts; and the fourth involved submitting it to priority stakeholders for evaluation and validation. After its updates, the indicators list was evaluated using Principal Component Analysis. All these stages allowed for the triangulation of information and the creation of a final list containing 48 sustainability indicators for pension funds, with information requested by priority stakeholders. This allows regulators to adjust disclosure rules, including those required by stakeholders and good governance practices. It also allows pension funds to identify the indicators required by stakeholders, reducing information asymmetry. The adoption of the list of indicators would promote trust, legitimacy, and sustainability for pension funds.
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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 | 0.030 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.029 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".