Bibliographic record
Abstract
In 2008, in discussing issues of extraterritorial application of American employee benefits law, I wrote: “Global employee benefits law is an emerging field of study.” No longer is this the case. As the recent publication of a casebook on global employee benefit law, and now this series of comparative pension law papers, prove, this field of study is rapidly expanding. Conferences, lectures, and symposiums are being held not only in the United States, but across the globe concerning how to make employee benefits more secure through learning from the pension experiences of other countries.In thinking about how to systematically study the field of global employee benefits law, many have maintained that we should consider the values that countries seek to promote in providing pension and other types of benefits to their citizens and employees. This “values perspective” emphasizes the values of responsibility, protection, solidarity, non-discrimination, and participation. The papers in this symposium by Professors Muir, Moore, and Davis consider the values that underlie pension systems in the United States, Canada, and Australia. By considering these pension values, these papers help to explain not only why the recent global financial crisis adversely impacted so many countries' pension systems, but also why some systems were better able to respond to the challenge posed by this crisis than others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 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".