Enhancing Reputational Risk Management
Bibliographic record
Abstract
This report highlights the importance of reputational risk management in modern tax administration and sets out some key considerations as to how to identify and manage reputational risks. It also contains a set of tools to assist tax administrations in developing their capacity in this area, including a maturity model which allows administrations to self-assess their current capacity and to identify areas for possible further development. The report has been produced by the FTA Enterprise RiskManagement Community of Interest (COI). It is the first in an intended series of reports by the FTA’s Communities of Interest which bring together experts to exchange views and work collaboratively on major themes of modern tax administration. This work was led within the Enterprise Risk Management COI by colleagues from the Canada Revenue Agency. As noted in the report, managing reputational risk is hugely important in helping to achieve the objectives of tax administration and wider government, something which is particularly true in times of crisis. The key principles driving reputational risk are trust in the administration and its staff and respect towards the organisation. When an administration consistently abides by its ethical duties, it establishes trust in the eyes of taxpayers and other stakeholders. When it fails to meet the standards expected of it, particularly with respect to the fair and equal treatment of taxpayers, public trust and credibility can be quickly eroded.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".