Performance Appraisal Model for Pension Fund: Before and After the Application of Good Pension Fund Governance in the Perspective of Political Economy of Accounting
Bibliographic record
Abstract
This research has long-term objective to reconstruct performance appraisal model for pension fund by taking into account the fairness of distribution of power and wealth of related parties, namely the employers, pension fund trustees, and pension fund participants. The specific objective of this research was to elaborate the factors namely fairness of power and wealth distribution of related parties, namely the employers, pension fund trustees, and pension fund participants before and after the implementation of Good Pension Fund Governance (GPFG). This is a descriptive qualitative research by nature using the theory of critical paradigm of Political Economy of Accounting (PEA). Data collection technique in this study was carried out directly and indirectly on the Pension Fund of Merdeka University Malang, Central Association of Pension Fund Indonesia (ADPI), KOMDA VI East Java and surroundings, as well as the Financial Services Authority (OJK). Sources of data for this research were obtained from informants through observation and interviews in the form of both financial data and also non-financial. Results of this study are to formulate performance appraisal model for pension fund before GPFG where the fairness of power and wealth distribution of the employers, pension fund trustees and pension fund participants, including general management, administration of financial and participation as well as accounting and investments, while the formulation of the evaluation model of pension funds performance after GPFG where fairness of power and wealth distribution of the employers, pension fund trustees and pension fund participants including sixteen guidelines for the application of GPFG.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".