Although public health scholars increasingly recognize the importance of the social determinants of health (SDOH), health policy outputs tend to emphasize downstream lifestyle factors instead. We use an automated corpus research approach to analyse fourteen years of health policy debate in the Dutch House of Representatives\' Health Committee, testing three potential causes of the lack of attention for SDOH: political ideology, by which members of parliament (MPs) from some political orientations may prioritize lifestyle factors over SDOH; lifestyle drift, by which early attention for SDOH during problem analysis is replaced by a lifestyle focus in the development of solutions as the challenges in addressing SDOH become clear; and focusing events, by which political or societal chance events, known to the public and political elites simultaneously, bolster the lifestyle perspective on health. Our analysis shows that overall, the committee spent most of its time discussing neither SDOH nor lifestyle: healthcare financing and service delivery dominated instead. When SDOH or lifestyle were referenced, left-leaning MPs referred significantly more to SDOH and right-leaning MPs significantly more to lifestyle. Temporal effects related to election cycles yielded inconsistent evidence. Finally, peak attention for both lifestyle and SDOH coincided with ongoing political debate instead of exogenous, unforeseen focusing events, and these peaks were rendered relatively insignificant by the larger and more consistent attention for health care. This paper provides a first step toward automated analysis of policy debates at scale, opening up new avenues for the empirical study of health political discourse.

This paper discusses how the transcription hurdle in dialect corpus building can be cleared. While corpus analysis has strongly gained in popularity in linguistic research, dialect corpora are still relatively scarce. This scarcity can be attributed to several factors, one of which is the challenging nature of transcribing dialects, given a lack of both orthographic norms for many dialects and speech technological tools trained on dialect data. This paper addresses the questions (i) how dialects can be transcribed efficiently and (ii) whether speech technological tools can lighten the transcription work. These questions are tackled using the Southern Dutch dialects (SDDs) as case study, for which the usefulness of automatic speech recognition (ASR), respeaking, and forced alignment is considered. Tests with these tools indicate that dialects still constitute a major speech technological challenge. In the case of the SDDs, the decision was made to use speech technology only for the word-level segmentation of the audio files, as the transcription itself could not be sped up by ASR tools. The discussion does however indicate that the usefulness of ASR and other related tools for a dialect corpus project is strongly determined by the sound quality of the dialect recordings, the availability of statistical dialect-specific models, the degree of linguistic differentiation between the dialects and the standard language, and the goals the transcripts have to serve.