Putting Humpty Together Again: How Reputation Regulation Fails the Charitable Sector
Bibliographic record
Abstract
Abstract Investigations of how Oxfam Great Britain (GB) managed its safeguarding systems and handled revelations of sexual exploitation by its staff highlighted a variety of internal governance and culture issues, and a lack of transparency as it sought to protect its reputation. The current models of reputation management do not fully explain its actions, however. This article argues that five systemic factors in the environment in which nonprofits operate create undue pressures for protection of reputations and contribute to poor assessment of risks, inadequate accountability systems and limited transparency. These factors include: a stress on success and related competition for market share and pressures for growth; expectations of low overheads; challenges of governance and risk management; lack of public awareness; and regulatory gaps. Drawing on media coverage and the commissions of inquiry, the analysis shows how all of these contextual factors were at play in the Oxfam case, and suggests potential reforms.
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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 | 0.024 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.042 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".