Is big team research fair in national research assessments? The case of the UK Research Excellence Framework 2021

Collaborative research causes problems for research assessments because of the difficulty in fairly crediting its authors.Whilst splitting the rewards for an article amongst its authors has the greatest surface-level fairness,many important evaluations assign full credit to each author,irrespective of team size.The underlying rationales for this are labour reduction and the need to incentivise collaborative work because it is necessary to solve many important societal problems.This article assesses whether full counting changes results compared to fractional counting in the case of the UK’s Research Excellence Framework(REF)2021.For this assessment,fractional counting reduces the number of journal articles to as little as 10%of the full counting value,depending on the Unit of Assessment(UoA).Despite this large difference,allocating an overall grade point average(GPA)based on full counting or fractional counting gives results with a median Pearson correlation within UoAs of 0.98.The largest changes are for Archaeology(r=0.84)and Physics(r=0.88).There is a weak tendency for higher scoring institutions to lose from fractional counting,with the loss being statistically significant in 5 of the 34 UoAs.Thus,whilst the apparent over-weighting of contributions to collaboratively authored outputs does not seem too problematic from a fairness perspective overall,it may be worth examining in the few UoAs in which it makes the most difference.

“My ADHD Hellbrain”: A Twitter Data Science Perspective on a Behavioural Disorder

Purpose: Attention deficit hyperactivity disorder(ADHD) is a common behavioural condition. This article introduces a new data science method, word association thematic analysis, to investigate whether ADHD tweets can give insights into patient concerns and online communication needs. Design/methodology/approach: Tweets matching "my ADHD"(n=58,893) and 99 other conditions(n=1,341,442) were gathered and two thematic analyses conducted. Analysis 1: A standard thematic analysis of ADHD-related tweets. Analysis 2: A word association thematic analysis of themes unique to ADHD.Findings: The themes that emerged from the two analyses included people ascribing their brains agency to explain and justify their symptoms and using the concept of neurodivergence for a positive self-image. Research limitations: This is a single case study and the results may differ for other topics.Practical implications: Health professionals should be sensitive to patients' needs to understand their behaviour, find ways to justify and explain it to others and to be positive about their condition.Originality/value: Word association thematic analysis can give new insights into the(self-reported) patient perspective.

I’m Nervous about Sharing This Secret with You:Youtube Influencers Generate Strong Parasocial Interactions by Discussing Personal Issues

Purpose:Performers may generate loyalty partly through eliciting illusory personal connections with their audience,parasocial relationships(PSRs),and individual illusory exchanges,parasocial interactions(PSIs).On social media,semi-PSIs are real but imbalanced exchanges with audiences,including through comments on influencers’videos,and strong semi-PSIs are those that occur within PSRs.This article introduces and assesses an automatic method to detect videos with strong PSI potential.Design/methodology/approach:Strong semi-PSIs were hypothesized to occur when commenters used a variant of the pronoun“you”,typically addressing the influencer.Comments on the videos of UK female influencer channels were used to test whether the proportion of you pronoun comments could be an automated indicator of strong PSI potential,and to find factors associating with the strong PSI potential of influencer videos.The highest and lowest strong PSI potential videos for 117 influencers were classified with content analysis for strong PSI potential and evidence of factors that might elicit PSIs.Findings:The you pronoun proportion was effective at indicating video strong PSI potential,the first automated method to detect any type of PSI.Gazing at the camera,head and shoulders framing,discussing personal issues,and focusing on the influencer associated with higher strong PSI potential for influencer videos.New social media factors found include requesting feedback and discussing the channel itself.Research limitations:Only one country,genre and social media platform was analysed.Practical implications:The method can be used to automatically detect YouTube videos with strong PSI potential,helping influencers to monitor their performance.Originality/value:This is the first automatic method to detect any aspect of PSI or PSR.