Organisational responses to mandatory modern slavery disclosure legislation: a failure of experimentalist governance?
Bibliographic record
Abstract
Purpose This paper investigates how organisations are responding to mandatory modern slavery disclosure legislation. Experimentalist governance suggests that organisations faced with disclosure requirements such as those contained in the UK Modern Slavery Act 2015 will compete with one another, and in doing so, improve compliance. The authors seek to understand whether this is the case. Design/methodology/approach This study is set in the UK public sector. The authors conduct interviews with over 25% of UK universities that are within the scope of the UK Modern Slavery Act 2015 and examine their reporting and disclosure under that legislation. Findings The authors find that, contrary to the logic of experimentalist governance, universities' disclosures as reflected in their modern slavery statements are persistently poor on detail, lack variation and have led to little meaningful action to tackle modern slavery. They show that this is due to a herding effect that results in universities responding as a sector rather than independently; a built-in incapacity to effectively manage supply chains; and insufficient attention to the issue at the board level. The authors also identity important boundary conditions of experimentalist governance. Research limitations/implications The generalisability of the authors’ findings is restricted to the public sector. Practical implications In contexts where disclosure under the UK Modern Slavery Act 2015 is not a core offering of the sector, and where competition is limited, there is little incentive to engage in a “race to the top” in terms of disclosure. As such, pro-forma compliance prevails and the effectiveness of disclosure as a tool to drive change in supply chains to safeguard workers is relatively ineffective. Instead, organisations must develop better knowledge of their supply chains and executives and a more critical eye for modern slavery to be combatted effectively. Accountants and their systems and skills can facilitate this development. Originality/value This is the first investigation of the organisational processes and activities which underpin disclosures related to modern slavery disclosure legislation. This paper contributes to the accounting and disclosure modern slavery literature by investigating public sector organisations' processes, activities and responses to mandatory reporting legislation on modern slavery.
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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 | 0.128 | 0.251 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| 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".