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Abstract
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BACKGROUND: Significant research attention has been given to understanding the processes of drug policy reform. However, there has been surprisingly little analysis of the persistence of policy in the face of opposition and evidence of ineffectiveness. In this article we analysed just such a case - police drug detection dog policy in NSW, Australia. We sought to identify factors which may account for the continuation of this policy, in spite of counter-evidence and concerted advocacy.
METHODS: The analysis was conducted using the Advocacy Coalition Framework (ACF). We collated documents relating to NSW drug detection dog policy from 1995 to 2016, including parliamentary records (NSW Parliament Hansard), government and institutional reports, legislation, police procedures, books, media, and academic publications. Texts were then read, coded and classified against the core dimensions of the ACF, including subsystem actors and coalitions, their belief systems and resources and venues employed for policy debate.
RESULTS: Three coalitions were identified as competing in the policy subsystem: security/law and order, civil liberties and harm reduction. Factors that aided policy stability were the continued dominance of the security/law and order coalition since they introduced the drug dog policy; a power imbalance enabling the ruling coalition to limit when and where the policy was discussed; and a highly adversarial policy subsystem. In this context even technical knowledge that dogs infringed civil liberties and increased risks of overdose were readily downplayed, leading to only incremental changes in implementation rather than policy cessation or wholesale revision.
CONCLUSION: The analysis provides new insights into why the accumulation of new evidence and advocacy efforts can be insufficient to drive significant policy change. It poses a challenge for the evidence-based paradigm suggesting that in highly adversarial policy subsystems new evidence is unlikely to generate policy change without broader subsystem change, such as reducing the adversarial nature and/or providing new avenues for cross-coalition learning.
Copyright © 2017 Elsevier B.V. All rights reserved.
Language: en |