The UK Pensions Crisis and Institutional Innovation: Beyond Corporatism and Neoliberalism
Bibliographic record
Abstract
With the defeat of the UK Labour government in May 2010, the incoming coalition government of Conservatives and Liberal Democrats contemplated abandoning the National Employee Savings Trust (NEST). 2 Conceived by the Labour government’s Pensions Commission (2005), Lord Turner’s expert panel recommended the establishment of NEST as a way of addressing the pensions crisis: the future liabilities associated with government-provided social security, declining occupational pension coverage rates, and the accelerated closure of defined benefit pension schemes in the private sector (Pemberton et al., 2006). In effect, NEST is a state-sponsored pension reserve scheme for working men and women via automatic enrollment at their place of work. Although not mandatory, as is the case in Australia, Canada, and Sweden (Cronqvist and Thaler, 2004), it was designed to encourage private-sector employees to save for their retirement (in the manner suggested by Thaler and Sunstein, 2003). After a review, the coalition government affirmed the previous government’s commitment to NEST (which began in 2012). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".