Las mediciones y la calidad de la información contable: un análisis desde la perspectiva de la regulación contable internacional
Bibliographic record
Abstract
En el presente trabajo se trata la calidad de la información contable, especialmente desde el punto de vista de las mediciones monetarias que brindan los estados contables publicados siguiendo estándares de aceptación generalizada. En particular, se analiza la existencia de heterogeneidad de criterios de medición bajo normas internacionales de información financiera (NIIF) para los distintos elementos integrantes del patrimonio de un ente. En el trabajo se analizan los desarrollos teóricos de distintos reguladores (Canadian Accounting Standards Board –AcSB-, Internacional Accounting Standards Board –IASB- y el Financial Accounting Standards Board –FASB-) y se realiza un estudio empírico sobre la existencia de distintas bases de medición. Por último, se brindan un conjunto de sugerencias que, a juicio del autor, permitirían mejorar significativamente la información producida por los sistemas contables.
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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 | 0.018 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".