Disminución en el ciclo de liquidación y la liquidez en la Bolsa de Valores de Lima
Bibliographic record
Abstract
Mi experiencia laboral se ha desarrollado en el Grupo BVL (GBVL), iniciando como \npracticante de desarrollo de proyectos en su subsidiaria Bolsa de Valores de Lima \n(BVL), apoyando en la elaboración de reportes e informes de potenciales productos \nfinancieros y negocios. Posterior a ello, pasé a ser ejecutivo junior de servicios \ninternacionales en el Registro Central de Valores y Liquidaciones (CAVALI) \ntrabajando en temas relacionados a la custodia, compensación y liquidación de los \nvalores internacionales en los depósitos de Depository Trust & Clearing Corporation \n(DTCC) y The Canadian Depository for Securities (CDS). En este informe se \nevaluará si es necesario que el mercado peruano se prepare para el nuevo ciclo de \nliquidación T+1. Para aquello se evaluará si la reducción en el ciclo de liquidación \nperuano de T+3 a T+2 ha tenido un efecto positivo en la liquidez de las operaciones \nde rueda efectivo de la Bolsa de Valores de Lima.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".