The Performance of Mutual Fund Schemes in The Framework of Risk and Return
Bibliographic record
Abstract
In 12th century france, the subjects substitute have been worrid over directing and planning the duties of nation clusters in light of an authentic worry for the banks.considering the way that these individual besides exchange with duties, they may be hilnted as the essential vendor. Inside the point of convergence of the 13th century, Venture money related pros started to substitute in official securities. In 1335 the lawmaking body limited spreading gossipy treats proposed to cut the cost of association stores. Delegates in pisa, Verona, Genoa and Florence in addition started exchngeing official securities all through the 14th century. This was best practical given by that those have been reasonable city imparts never again coordinated by procedure for a duke at any rate a gathering of persuading local people. Italian affliation have been besides the first to piece shares. relationship in England and the low countries grasped in the 16 th century. There are to a great degree securities trades in a general sense each made and most working up econom ie, with the division’s most noteworthy markets being in the USA, joined kingdom, japan, india, china, Canada, Germany (Frankfurt stock exchange), france, south korea and the netherland.
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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".