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The paper aims to analyse the meaning and extension of discretionary power of social service professionals within network-based interventions.

Empirically, the paper is based on a case study of a network-based policy involving private and public organisations in the Northeast of Italy (Province of Trento).

The paper identifies netocracy as a social policy logic distinct from bureaucracy and professionalism. What legitimises netocracy is neither authority nor expertise but cooperation, the activation of connections and involvement, considered “good” per se. In this framework, professionalism and discretion acquire new and problematic meanings compared to street-level bureaucracy processes.

Based on a case study, the research results cannot be generalised but pave the way to further comparative investigations.

The paper reveals that the position of professionals in netocracy is to some extent trickier than that in a bureaucracy because netocracy seems to have the power to encapsulate them and make it less likely for them to deviate from expected courses of action.

Combining different literature streams – street level bureaucracy, professionalism, network organisations and welfare governance – and building on an original case study, the paper contribute to understanding professionalism in welfare contexts increasingly characterised by the combination of bureaucratic, professional and network logics.

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.