Effecten van IFRS 16 Leases op informatie in de jaarrekening
Bibliographic record
Abstract
Dit artikel onderzoekt effecten van de eerste toepassing van IFRS 16 op informatie gerelateerd aan leases in jaarrekeningen over 2019 bij lessees. IFRS 16 vereist meer informatie over leases waarvan een groot deel dikwijls wordt gegeven. Een ander deel echter, met name over toekomstige leasebetalingen, veel minder. Ook over gevolgen van door de invoering van IFRS 16 veranderde cijfers in de jaarrekening op impairment testing, alternatieve prestatiemaatstaven en bestuurdersbeloningen treffen we (heel) weinig informatie aan. Vaker besteden de controleverklaringen van de accountant bij de jaarrekening aandacht aan de invoering van IFRS 16. De grondslag voor verwerking van latente belastingposities als gevolg van IFRS 16 wordt weinig vermeld, is uiteenlopend en wacht op de aangekondigde regelgeving.
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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 | 0.020 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.055 | 0.006 |
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".