Bibliographic record
Abstract
In Conclusion: A Call to Arms T he 2001 to 2006 events described in the chapters of this book have created the necessary conditions for the pension revolution Peter Drucker foresaw 30 years ago.But these necessary conditions do not guarantee the revolution's success.Success demands that the decisions required to launch the revolution described in these pages actually be taken.And that in turn requires, borrowing Malcolm Gladwell's expression, ''tipping point'' conditions.Where do these conditions exist today, and who are the people in positions to take revolutionary decisions about changing pension design, organizational structure, investment beliefs, risk management, performance measurement, and results disclosure?In answering this question, it is useful to first observe that ''tipping point'' opportunities exist both outside and inside pension organizations.Examples of the former lie in the legislative and regulatory spheres, as well as in bodies responsible for setting ''generally accepted'' standards in accounting, actuarial, and pension management practices.One of the chapters in the book noted that it is undoubtedly the Dutch who are showing that radical change in the pension paradigm can be effected through legislative and regulatory means.With the tabling of the Turner Commission report, followed by HM Treasury's White Paper on pension reform, the United Kingdom is now seriously contemplating material change.On the other side of the Atlantic, Quebec's pension regulator Régie des Rentes is the first North American regulator to also contemplate playing a more activist role in changing the pension paradigm.On the ''generally accepted'' practices front, the accounting profession appears finally ready to move pension-related financial disclosures to a mark-to-market basis.This will be an important step toward finally disentangling the murky web of contingent claims embedded in traditional defined benefit (DB) plans.There are also ''tipping point'' opportunities inside pension organizations themselves.These opportunities divide naturally into corporate and industry/public-sector contexts.In the corporate sector, the noted coming changes in pension accounting will provide further impetus to the closing of traditional DB plans there.An important unresolved question is what role corporations will now play to facilitate the accumulation of adequate future 335 Pension Revolution: A Solution to the Pensions Crisis By Keith P. Ambachtsheer
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.019 | 0.031 |
| Insufficient payload (model declined to judge) | 0.104 | 0.067 |
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".