Reporting of research findings is often selective. This threatens the validity of the published body of knowledge if the decision to report depends on the nature of the results. The evidence derived from studies on causes and mechanisms underlying selective reporting may help to avoid or reduce reporting bias. Such research should be guided by a theoretical framework of possible causal pathways that lead to reporting bias. We build upon a classification of determinants of selective reporting that we recently developed in a systematic review of the topic. The resulting theoretical framework features four clusters of causes. There are two clusters of necessary causes: (A) motivations (e.g. a preference for particular findings) and (B) means (e.g. a flexible study design). These two combined represent a sufficient cause for reporting bias to occur. The framework also features two clusters of component causes: (C) conflicts and balancing of interests referring to the individual or the team, and (D) pressures from science and society. The component causes may modify the effect of the necessary causes or may lead to reporting bias mediated through the necessary causes. Our theoretical framework is meant to inspire further research and to create awareness among researchers and end-users of research about reporting bias and its causes.
![Causes of reporting bias: a theoretical framework [version 2; peer review: 2 approved]](https://publinova-harvester-content-acc.s3.amazonaws.com/thumbnails/files/previews/pdf/20240409023725931111.68f_a863_4e94_84cd_cf2a63540cea_18310_jenny_van_der_stee-thumbnail-400x300.png)
In December of 2004 the Directorate General for Research and Technological Development (DG RTD) of the European Commission (EC) set up a High-Level Expert Group to propose a series of measures to stimulate the reporting of Intellectual Capital in research intensive Small and Medium-Sized Enterprises (SMEs). The Expert Group has focused on enterprises that either perform Research and Development (R&D), or use the results of R&D to innovate and has also considered the implications for the specialist R&D units of larger enterprises, dedicated Research & Technology Organizations and Universities. In this report the Expert Group presents its findings, leading to six recommendations to stimulate the reporting of Intellectual Capital in SMEs by raising awareness, improving reporting competencies, promoting the use of IC Reporting and facilitating standardization.
It is crucial that ASR systems can handle the wide range of variations in speech of speakers from different demographic groups, with different speaking styles, and of speakers with (dis)abilities. A potential quality-of-service harm arises when ASR systems do not perform equally well for everyone. ASR systems may exhibit bias against certain types of speech, such as non-native accents, different age groups and gender. In this study, we evaluate two widely-used neural network-based architectures: Wav2vec2 and Whisper on potential biases for Dutch speakers. We used the Dutch speech corpus JASMIN as a test set containing read and conversational speech in a human-machine interaction setting. The results reveal a significant bias against non-natives, children and elderly and some regional dialects. The ASR systems generally perform slightly better for women than for men.
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