Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bond laddering and bond indexing have been widely accepted approaches to bond investing among retail investors. However, bond laddering has virtually been ignored in both the academic literature and most of the popular investment textbooks. One thing both approaches have in common is that they are passive strategies with no attempt whatsoever to beat the market. There are many unresolved issues about the two seemingly similar approaches. First, which approach should an investor favor? Is there any room for both to be used at the same time? Second, if an investor decides to use a ladder, what is the appropriate term to maturity for the ladder? There is hardly any theoretical or empirical guidance as to which is a better approach to use and the right term of a ladder. The relative attractiveness of the above two approaches are empirically examined in this study. We identify conditions that favor one over the other. Conditions under which both instruments should be held within an optimal portfolio are also identified. We also identify conditions in which a longer term ladder is more appropriate than a shorter term ladder.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it