Étiquette : social sciences

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EN

What Constitutes Authorship in the Social Sciences?

Author : Gernot Pruschak

Authorship represents a highly discussed topic in nowadays academia. The share of co-authored papers has increased substantially in recent years allowing scientists to specialize and focus on specific tasks.

Arising from this, social scientific literature has especially discussed author orders and the distribution of publication and citation credits among co-authors in depth. Yet only a small fraction of the authorship literature has also addressed the actual underlying question of what actually constitutes authorship.

To identify social scientists’ motives for assigning authorship, we conduct an empirical study surveying researchers around the globe. We find that social scientists tend to distribute research tasks among (individual) research team members. Nevertheless, they generally adhere to the universally applicable Vancouver criteria when distributing authorship.

More specifically, participation in every research task with the exceptions of data work as well as reviewing and remarking increases scholars’ chances to receive authorship. Based on our results, we advise journal editors to introduce authorship guidelines that incorporate the Vancouver criteria as they seem applicable to the social sciences.

We further call upon research institutions to emphasize data skills in hiring and promotion processes as publication counts might not always depict these characteristics.

URL : What Constitutes Authorship in the Social Sciences?

DOI : https://doi.org/10.3389/frma.2021.655350

Catégories
EN

An empirical assessment of transparency and reproducibility-related research practices in the social sciences (2014–2017)

Authors : Tom E. Hardwicke, Joshua D. Wallach, Mallory C. Kidwell, Theiss Bendixen, Sophia Crüwell, John P. A. Ioannidis

Serious concerns about research quality have catalysed a number of reform initiatives intended to improve transparency and reproducibility and thus facilitate self-correction, increase efficiency and enhance research credibility.

Meta-research has evaluated the merits of some individual initiatives; however, this may not capture broader trends reflecting the cumulative contribution of these efforts.

In this study, we manually examined a random sample of 250 articles in order to estimate the prevalence of a range of transparency and reproducibility-related indicators in the social sciences literature published between 2014 and 2017.

Few articles indicated availability of materials (16/151, 11% [95% confidence interval, 7% to 16%]), protocols (0/156, 0% [0% to 1%]), raw data (11/156, 7% [2% to 13%]) or analysis scripts (2/156, 1% [0% to 3%]), and no studies were pre-registered (0/156, 0% [0% to 1%]).

Some articles explicitly disclosed funding sources (or lack of; 74/236, 31% [25% to 37%]) and some declared no conflicts of interest (36/236, 15% [11% to 20%]). Replication studies were rare (2/156, 1% [0% to 3%]).

Few studies were included in evidence synthesis via systematic review (17/151, 11% [7% to 16%]) or meta-analysis (2/151, 1% [0% to 3%]). Less than half the articles were publicly available (101/250, 40% [34% to 47%]).

Minimal adoption of transparency and reproducibility-related research practices could be undermining the credibility and efficiency of social science research. The present study establishes a baseline that can be revisited in the future to assess progress.

URL : An empirical assessment of transparency and reproducibility-related research practices in the social sciences (2014–2017)

DOI : https://doi.org/10.1098/rsos.190806

Catégories
EN

Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015

Authors : Colin F. Camerer, Anna Dreber, Felix Holzmeister, Teck-Hua Ho, Jürgen Huber, Magnus Johannesson, Michael Kirchler, Gideon Nave, Brian Nosek, Thomas Pfeiffer, Adam Altmejd, Nick Buttrick, Taizan Chan, Yiling Chen, Eskil Forsell, Anup Gampa, Emma Heikensten, Lily Hummer, Taisuke Imai, Siri Isaksson, Dylan Manfredi, Julia Rose, Eric-Jan Wagenmakers, Hang Wu

Being able to replicate scientific findings is crucial for scientific progress. We replicate 21 systematically selected experimental studies in the social sciences published in Nature and Science between 2010 and 2015.

The replications follow analysis plans reviewed by the original authors and pre-registered prior to the replications. The replications are high powered, with sample sizes on average about five times higher than in the original studies.

We find a significant effect in the same direction as the original study for 13 (62%) studies, and the effect size of the replications is on average about 50% of the original effect size. Replicability varies between 12 (57%) and 14 (67%) studies for complementary replicability indicators.

Consistent with these results, the estimated true positive rate is 67% in a Bayesian analysis. The relative effect size of true positives is estimated to be 71%, suggesting that both false positives and inflated effect sizes of true positives contribute to imperfect reproducibility.

Furthermore, we find that peer beliefs of replicability are strongly related to replicability, suggesting that the research community could predict which results would replicate and that failures to replicate were not the result of chance alone.

URL : Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015

DOI : https://doi.org/10.1038/s41562-018-0399-z

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FR

D’abord les données, ensuite la méthode ? Big data et déterminisme en sciences sociales

Auteurs/Authors : Jean-Christophe Plantin, Federica Russo

Si les chercheurs en sciences sociales ont depuis longtemps recours à de larges quantités de données, par exemple avec les enquêtes par questionnaire, le recours à des données numériques massives et hétérogènes, ou « big data », est de plus en plus fréquent.

À travers un abandon de la théorie pour la recherche de corrélations, cette multitude de données suscite-t-elle une nouvelle forme de déterminisme ?

L’histoire des sciences sociales indique au contraire que l’accroissement des données disponibles a entraîné un rejet progressif d’une hypothèse déterministe héritée des sciences de la nature, au profit d’une autonomisation méthodologique fondée sur la modélisation statistique.

Dans ce contexte, cet article montre que l’accent mis sur la taille des big data ne signifie pas tant un retour au déterminisme, mais est davantage révélateur du désajustement actuel entre les caractéristiques de ces données massives et les méthodes et infrastructures en sciences sociales.

URL : https://socio.revues.org/2328

Catégories
EN

Afraid of Scooping – Case Study on Researcher Strategies against Fear of Scooping in the Context of Open Science

Author : Heidi Laine

The risk of scooping is often used as a counter argument for open science, especially open data. In this case study I have examined openness strategies, practices and attitudes in two open collaboration research projects created by Finnish researchers, in order to understand what made them resistant to the fear of scooping.

The radically open approach of the projects includes open by default funding proposals, co-authorship and community membership. Primary sources used are interviews of the projects’ founding members.

The analysis indicates that openness requires trust in close peers, but not necessarily in research community or society at large. Based on the case study evidence, focusing on intrinsic goals, like new knowledge and bringing about ethical reform, instead of external goals such as publications, supports openness.

Understanding fundaments of science, philosophy of science and research ethics, can also have a beneficial effect on willingness to share. Whether there are aspects in open sharing that makes it seem riskier from the point of view of certain demographical groups within research community, such as women, could be worth closer inspection.

URL : Afraid of Scooping – Case Study on Researcher Strategies against Fear of Scooping in the Context of Open Science

DOI : http://doi.org/10.5334/dsj-2017-029

Catégories
EN

Towards an Ethical Framework for Publishing Twitter Data in Social Research: Taking into Account Users’ Views, Online Context and Algorithmic Estimation

Authors : Matthew L Williams, Pete Burnap, Luke Sloan

New and emerging forms of data, including posts harvested from social media sites such as Twitter, have become part of the sociologist’s data diet. In particular, some researchers see an advantage in the perceived ‘public’ nature of Twitter posts, representing them in publications without seeking informed consent.

While such practice may not be at odds with Twitter’s terms of service, we argue there is a need to interpret these through the lens of social science research methods that imply a more reflexive ethical approach than provided in ‘legal’ accounts of the permissible use of these data in research publications.

To challenge some existing practice in Twitter-based research, this article brings to the fore: (1) views of Twitter users through analysis of online survey data; (2) the effect of context collapse and online disinhibition on the behaviours of users; and (3) the publication of identifiable sensitive classifications derived from algorithms.

URL : Towards an Ethical Framework for Publishing Twitter Data in Social Research: Taking into Account Users’ Views, Online Context and Algorithmic Estimation

DOI : http://dx.doi.org/10.1177%2F0038038517708140

Catégories
EN

Advancing research data publishing practices for the social sciences: from archive activity to empowering researchers

Authors : Veerle Van den Eynden, Louise Corti

Sharing and publishing social science research data have a long history in the UK, through long-standing agreements with government agencies for sharing survey data and the data policy, infrastructure, and data services supported by the Economic and Social Research Council.

The UK Data Service and its predecessors developed data management, documentation, and publishing procedures and protocols that stand today as robust templates for data publishing.

As the ESRC research data policy requires grant holders to submit their research data to the UK Data Service after a grant ends, setting standards and promoting them has been essential in raising the quality of the resulting research data being published. In the past, received data were all processed, documented, and published for reuse in-house.

Recent investments have focused on guiding and training researchers in good data management practices and skills for creating shareable data, as well as a self-publishing repository system, ReShare. ReShare also receives data sets described in published data papers and achieves scientific quality assurance through peer review of submitted data sets before publication.

Social science data are reused for research, to inform policy, in teaching and for methods learning. Over a 10 years period, responsive developments in system workflows, access control options, persistent identifiers, templates, and checks, together with targeted guidance for researchers, have helped raise the standard of self-publishing social science data.

Lessons learned and developments in shifting publishing social science data from an archivist responsibility to a researcher process are showcased, as inspiration for institutions setting up a data repository.

URL : Advancing research data publishing practices for the social sciences: from archive activity to empowering researchers

DOI : doi:10.1007/s00799-016-0177-3