Daniel Gilling Page 69
Crime Prevention and Community Safety: An International Journal
mise the very highest scientific standards because — unsurprisingly, for those aware of the
general state of crime prevention evaluations — so few evaluations match the very highest
ranking, with none obtaining the gold standard. Instead, the authors suggest quite reasonably
that where a crime prevention measure has obtained at least two separate 3-ranked evaluations
demonstrating success, it may be assumed to be something that works. By the same token,
those measures obtaining at least two separate 3-ranked evaluations showing failure may be
deemed not to work; those achieving a single 3-ranked evaluation demonstrating success may
be characterised as promising; and those achieving no such ranked evaluation cannot be as-
sumed either to work or not to work.
When this ranking schema is applied to crime prevention measures in each of the institutional
contexts covered in the main body of the report, the result is the finding that not as much is
known about what works and what does not than could and perhaps should have been the case.
It becomes immediately apparent, especially in some of the institutional contexts where evalu-
ations inevitably tend to be more difficult, that evaluations are frequently not conducted, or
frequently not conducted very well, or with much scientific rigour. This is attributed by the
authors of this report in large part to the limited availability of funding for the purposes of
project evaluations. As they observe, ‘the major limitations on better crime prevention evalu-
ations today are not technical, but statutory’. That is to say that there now exists a considerable
knowledge of and expertise in the science of evaluation, but too frequently projects are resourced
without sufficient regard to or funding of the evaluation dimension, and with predictable con-
sequences.
The authors use this fact to make an important point to the National Institute of Justice. If
federally-funded programmes are resourced enough to facilitate proper and rigorous scientific
evaluations, then they will have a value well in excess of their financial cost, showing more
clearly than hitherto which crime prevention measures, in which institutional contexts, are
most effective. Such information allows lessons to be learnt.
The report’s findings are inevitably equivocal, given the paucity of a large proportion of the
evaluations, or their complete absence, yet there is more than enough here to indicate a few
important conclusions. It is apparent, for example, that crime prevention in institutional set-
tings other than policing and criminal justice shows enough promise to justify a decisive shift
away from the current over-reliance on these two as the mainstays of federally-funded crime
prevention — yet the authors acknowledge that crime prevention is not always the sole goal of
this federal funding. Also, it is apparent that crime prevention in one institutional setting is
interrelated with crime prevention in another, with the one reinforcing the other. This is a
simple, easily illustrated point, helping to explain why multi-strategies are often more effec-
tive than single measures, even though it makes it more difficult for the evaluator to separate
chains of cause and effect.
The findings of the report, about the poor state of evaluations generally, and the resultant lack
of knowledge of ‘what works’, are especially timely for those seeking to devise and imple-
ment crime prevention strategies in the UK in the wake of the 1998 Crime and Disorder Act.
This hastily conceived legislation clearly expects evaluation to be built into the strategies, and
yet with no additional central funding earmarked for crime prevention measures themselves,
let alone their evaluation, the prospects do not look good, and so the sound advice of this
report will probably pass unheeded too often.
This is particularly likely to be the case given the early signs that partnerships are basing their
identification of strategic priorities on the basis of very limited data sets, which will make