Bibliographic record
Abstract
6.For each jurisdiction, the review covers the domestic legal and administrative framework, the exchange of information framework and measures in place to ensure the confidentiality and appropriate use of CbC reports. Key findings 7.The key findings of the fifth annual peer review are as follows: Domestic legal and administrative framework: Over 100 jurisdictions have a domestic legal framework for CbC reporting in place.In addition, a number of jurisdictions have final legislation approved that is awaiting official publication.In this peer review report, 28 jurisdictions have received a general recommendation to put in place or finalise their domestic legal or administrative framework and 27 jurisdictions received one or more recommendations for improvements to specific areas of their framework.Furthermore, two jurisdictions finalised legislation during this peer review period but it has not been possible to carry out a review of that legislation.A review of the legislation will take place in the next peer review. Exchange of information framework: Of the jurisdictions included in this review, 82 jurisdictions have multilateral or bilateral competent authority agreements in place. Confidentiality: Of the jurisdictions included in this review, 88 have undergone an assessment by the Global Forum on Transparency and Exchange of Information for Tax Purposes (the Global Forum) concerning confidentiality and data safeguards in the context of implementing the AEOI standard, and did not receive any action plan. Appropriate use: Of the jurisdictions included in this review, 64 jurisdictions have provided detailed information, enabling the Inclusive Framework to obtain sufficient assurance that measures are in place to ensure the appropriate use of CbC reports. 8.During the course of this peer review, a number of jurisdictions reported delays in the implementation of CbC reporting, or in the filing and exchange of CbC reports, resulting from the impact of the COVID-19 pandemic.As these concern issues beyond the control of tax administrations, and there is no reason to believe they will persist once the pandemic comes to an end, these delays are not highlighted in each jurisdiction's peer review and no recommendation is made.If delays continue into periods covered by future peer reviews, they will be considered in the context of the global situation at that time. 9.A number of Inclusive Framework members are not included in this peer review report, either because they joined the Inclusive Framework after 1 October 2021 (at which point it was too late to incorporate them into the current peer review process) or they opted out of the peer review in accordance with the peer review terms of reference.Jurisdictions opting out of the peer review are required to confirm that they do not have any resident entities that are the UPE of an MNE Group above the consolidated group revenue threshold and that they will not require local filing of CbC reports.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.614 | 0.369 |
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".