Evidence map›Paper›PMID 34646220›Full record

ArticleFrontiers in psychology2021

Fitness for Purpose in Online Communities: Community Complexity Framework for Diagnosis and Design of Socio-Technical Systems.

Agnieszka Rychwalska, Magdalena Roszczyńska-Kurasińska, Karolina Ziembowicz, Jeremy V Pitt

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 45% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 6 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

Agnieszka RychwalskaRobert Zajonc Institute for Social Studies, University of Warsaw, Warsaw, Poland.
Magdalena Roszczyńska-KurasińskaRobert Zajonc Institute for Social Studies, University of Warsaw, Warsaw, Poland.
Karolina ZiembowiczInstitute of Psychology, The Maria Grzegorzewska University, Warsaw, Poland.
Jeremy V PittDepartment of Electrical & Electronic Engineering, Imperial College London, London, United Kingdom.
University of Warsaw · PLImperial College London · GBInstitute of Psychology · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent discourse on Information and Communication Technologies' (ICT) impact on societies has been dominated by negative side-effects of information exchange in huge online social systems. Yet, the size of ICT-based communities also provides an unprecedented opportunity for collective action, as exemplified through crowdfunding, crowdsourcing, or peer production. This paper aims to provide a framework for understanding what makes online collectives succeed or fail in achieving complex goals. The paper combines social and complexity sciences' insights on structures, mechanics, and emergent phenomena in social systems to define a Community Complexity Framework for evaluating three crucial components of complexity: multi-level structuration, procedural self-organization, and common identity. The potential value of such a framework would be to shift the focus of efforts aimed at curing the malfunctions of online social systems away from the design of algorithms that can automatically solve such problems, and toward the development of technologies which enable online social systems to self-organize in a more productive and sustainable way.

Indexed as

collective actioncollective awarenessemergenceICTsocial complexitysocial self-organizationsocio-technical systems

Identifiers

PMID34646220
PMCPMC8502862
OpenAlexW3202938926

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.