Can evidence-based healthy housing interventions be put into practice in the UK's private housing sector?

The UK’s public health agenda has encouraged enhanced housing and health interventions. The private housing sector (privately rented and owner occupied) is the favoured and majority UK tenure, but is it seen as a primarily health promoting environment, or a commercial asset? There has been a growing interest in integrating health and housing policy in recent years. However, housing and public health fall under separate government departments and funding regimes. Partnership working has sought to overcome silo working and encourage evidence-based practice, yet is particularly challenging for interventions in the private housing sector, with an increased emphasis on ‘personal responsibility’ for conditions. Strategic public health frameworks are in place, but barriers remain and there is pressure for organisations to revert to core activities. An accessible, continually updated evidence base specific to private sector housing is recommended, to help estimate health gain arising from interventions to prioritise activities and address inequalities.

Kappa calculus and evidential strength: A note on Åqvist's logical theory of legal evidence

Lennart Åqvist (1992) proposed a logical theory of legal evidence, based on the Bolding-Ekelöf of degrees of evidential strength. This paper reformulates Åqvist's model in terms of the probabilistic version of the kappa calculus. Proving its acceptability in the legal context is beyond the present scope, but the epistemological debate about Bayesian Law isclearly relevant. While the present model is a possible link to that lineof inquiry, we offer some considerations about the broader picture of thepotential of AI & Law in the evidentiary context. Whereas probabilisticreasoning is well-researched in AI, calculations about the threshold ofpersuasion in litigation, whatever their value, are just the tip of theiceberg. The bulk of the modeling desiderata is arguably elsewhere, if one isto ideally make the most of AI's distinctive contribution as envisaged forlegal evidence research.