'Crackpots' and 'active researchers': The controversy over links between arXiv and the scientific blogosphere

Peer review in funding-by-lottery: A systematic overview and expansion

Despite the surging interest in introducing lottery mechanisms into decision-making procedures for science funding bodies, the discourse on funding-by-lottery remains underdeveloped and, at times, misleading. Funding-by-lottery is sometimes presented as if it were a single mechanism when, in reality, there are many funding-by-lottery mechanisms with important distinguishing features. Moreover, funding-by-lottery is sometimes portrayed as an alternative to traditional methods of peer review when peer review is still used within funding-by-lottery approaches. This obscures a proper analysis of the (hypothetical and actual) variants of funding-by-lottery and important differences amongst them. The goal of this article is to provide a preliminary taxonomy of funding-by-lottery variants and evaluate how the existing evidence on peer review might lend differentiated support for variants of funding-by-lottery. Moreover, I point to gaps in the literature on peer review that must be addressed in future research. I conclude by building off of the work of Avin in moving toward a more holistic evaluation of funding-by-lottery. Specifically, I consider implications funding-by-lottery variants may have regarding trust and social responsibility.

Directions in abusive language training data, a systematic review: Garbage in, garbage out

Data-driven and machine learning based approaches for detecting, categorising and measuring abusive content such as hate speech and harassment have gained traction due to their scalability, robustness and increasingly high performance. Making effective detection systems for abusive content relies on having the right training datasets, reflecting a widely accepted mantra in computer science: Garbage In, Garbage Out. However, creating training datasets which are large, varied, theoretically-informed and that minimize biases is difficult, laborious and requires deep expertise. This paper systematically reviews 63 publicly available training datasets which have been created to train abusive language classifiers. It also reports on creation of a dedicated website for cataloguing abusive language data hatespeechdata.com. We discuss the challenges and opportunities of open science in this field, and argue that although more dataset sharing would bring many benefits it also poses social and ethical risks which need careful consideration. Finally, we provide evidence-based recommendations for practitioners creating new abusive content training datasets.

Directions in abusive language training data, a systematic review: Garbage in, garbage out

Data-driven and machine learning based approaches for detecting, categorising and measuring abusive content such as hate speech and harassment have gained traction due to their scalability, robustness and increasingly high performance. Making effective detection systems for abusive content relies on having the right training datasets, reflecting a widely accepted mantra in computer science: Garbage In, Garbage Out. However, creating training datasets which are large, varied, theoretically-informed and that minimize biases is difficult, laborious and requires deep expertise. This paper systematically reviews 63 publicly available training datasets which have been created to train abusive language classifiers. It also reports on creation of a dedicated website for cataloguing abusive language data hatespeechdata.com. We discuss the challenges and opportunities of open science in this field, and argue that although more dataset sharing would bring many benefits it also poses social and ethical risks which need careful consideration. Finally, we provide evidence-based recommendations for practitioners creating new abusive content training datasets.

Where Does Open Science Lead Us During a Pandemic? A Public Good Argument to Prioritize Rights in the Open Commons

During the 2020 COVID-19 pandemic, open science has become central to experimental, public health, and clinical responses across the globe. Open science (OS) is described as an open commons, in which a right to science renders all possible scientific data for everyone to access and use. In this common space, capitalist platforms now provide many essential services and are taking the lead in public health activities. These neoliberal businesses, however, have a problematic role in the capture of public goods. This paper argues that the open commons is a community of rights, consisting of people and institutions whose interests mutually support the public good. If OS is a cornerstone of public health, then reaffirming the public good is its overriding purpose, and unethical platforms ought to be excluded from the commons and its benefits.

The future(s) of open science

Almost everyone is enthusiastic that 'open science' is the wave of the future. Yet when one looks seriously at the flaws in modern science that the movement proposes to remedy, the prospect for improvement in at least four areas are unimpressive. This suggests that the agenda is effectively to re-engineer science along the lines of platform capitalism, under the misleading banner of opening up science to the masses.