scholarly journals Creating a Breeding Ground for Compliance and Honest Reporting Under the Landing Obligation: Insights from Behavioural Science

2018 ◽  
pp. 219-236 ◽  
Author(s):  
Sarah B. M. Kraak ◽  
Paul J. B. Hart
Keyword(s):  
Breeding Ground ◽  
Behavioural Science

Crisis knowledge management: Reconfiguring the behavioural science community for rapid responding in the Covid-19 crisis

2020 ◽  
Author(s):  
Ulrike Hahn ◽  
David Lagnado ◽  
Stephan Lewandowsky ◽  
Nick Chater
Keyword(s):  
Knowledge Management ◽  
Evidence Based ◽  
Behavioural Science ◽  
High Stakes ◽  
Evidence Based Policy ◽  
Science Community ◽  
Minimal Damage ◽  
Support Evidence

The present crisis demands an all-out response if it is to be mastered with minimal damage. This means we, as the behavioural science community, need to think about how we can adapt to best support evidence-based policy in a rapidly changing, high-stakes environment. This piece is an attempt to initiate this process. The ‘recommendations’ made are first stabs that will hopefully be critiqued, debated and improved.

scholarly journals Job analysis: Time for a paradigm shift

1984 ◽  
Vol 15 (4) ◽  
pp. 220-224 ◽  
Author(s):  
C. J. Cogill
Keyword(s):  
Equal Opportunity ◽  
Job Analysis ◽  
Job Design ◽  
Validity And Reliability ◽  
Behavioural Science ◽  
Task Identity ◽  
Labour Practice ◽  
Job Content ◽  
Task Significance ◽  
Identity Task

Job analysis, a major form of job measurement, is essential for a whole range of job related personnel functions and is often central in debate and legislation surrounding fair labour practice, equal opportunity and pay. This article deals with the behavioural-science contributions to the field. Job analysis and particularly quantified job analysis is discussed in detail and some methodological issues are highlighted. The author also deals with job design, i.e. the measurement of job content for job-design purposes. Aspects like skill variety, task identity, task significance, autonomy and feedback are scrutinized. Problems and implications regarding validity and reliability are discussed.

Use of satellite imagery to identify southern right whales ( Eubalaena australis ) on a Southwest Atlantic Ocean breeding ground

2021 ◽  
Author(s):  
Audrey A. Corrêa ◽  
João H. Quoos ◽  
André S. Barreto ◽  
Karina R. Groch ◽  
Patricia P. B. Eichler
Keyword(s):  
Atlantic Ocean ◽  
Satellite Imagery ◽  
Breeding Ground ◽  
Southwest Atlantic ◽  
Eubalaena Australis ◽  
Right Whales ◽  
Southern Right Whales

scholarly journals Evidence-Based Digital Tools for Weight Loss Maintenance: The NoHoW Project

Obesity Facts ◽  
2021 ◽  
pp. 1-14
Author(s):  
R. James Stubbs ◽  
Cristiana Duarte ◽  
António L. Palmeira ◽  
Falko F. Sniehotta ◽  
Graham Horgan ◽  
...  
Keyword(s):  
Weight Loss ◽  
Behaviour Change ◽  
Digital Health ◽  
Self Regulation ◽  
Self Management ◽  
Behaviour Change Techniques ◽  
Evidence Based ◽  
Behavioural Science ◽  
Digital Architecture

<b><i>Background:</i></b> Effective interventions and commercial programmes for weight loss (WL) are widely available, but most people regain weight. Few effective WL maintenance (WLM) solutions exist. The most promising evidence-based behaviour change techniques for WLM are self-monitoring, goal setting, action planning and control, building self-efficacy, and techniques that promote autonomous motivation (e.g., provide choice). Stress management and emotion regulation techniques show potential for prevention of relapse and weight regain. Digital technologies (including networked-wireless tracking technologies, online tools and smartphone apps, multimedia resources, and internet-based support) offer attractive tools for teaching and supporting long-term behaviour change techniques. However, many digital offerings for weight management tend not to include evidence-based content and the evidence base is still limited. <b><i>The Project:</i></b> First, the project examined why, when, and how many European citizens make WL and WLM attempts and how successful they are. Second, the project employed the most up-to-date behavioural science research to develop a digital toolkit for WLM based on 2 key conditions, i.e., self-management (self-regulation and motivation) of behaviour and self-management of emotional responses for WLM. Then, the NoHoW trial tested the efficacy of this digital toolkit in adults who achieved clinically significant (≥5%) WL in the previous 12 months (initial BMI ≥25). The primary outcome was change in weight (kg) at 12 months from baseline. Secondary outcomes included biological, psychological, and behavioural moderators and mediators of long-term energy balance (EB) behaviours, and user experience, acceptability, and cost-effectiveness. <b><i>Impact:</i></b> The project will directly feed results from studies on European consumer behaviour, design and evaluation of digital toolkits self-management of EB behaviours into development of new products and services for WLM and digital health. The project has developed a framework and digital architecture for interventions in the context of EB tracking and will generate results that will help inform the next generation of personalised interventions for effective self-management of weight and health.

Exploring fake news identification using word and sentence embeddings

2021 ◽  
pp. 1-8
Author(s):  
V.T Priyanga ◽  
J.P Sanjanasri ◽  
Vijay Krishna Menon ◽  
E.A Gopalakrishnan ◽  
K.P Soman
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Network Analysis ◽  
Supervised Machine Learning ◽  
Breeding Ground ◽  
Fake News ◽  
Data Set ◽  
Highly Correlated ◽  
Use Of Social Media ◽  
The Liar

The widespread use of social media like Facebook, Twitter, Whatsapp, etc. has changed the way News is created and published; accessing news has become easy and inexpensive. However, the scale of usage and inability to moderate the content has made social media, a breeding ground for the circulation of fake news. Fake news is deliberately created either to increase the readership or disrupt the order in the society for political and commercial benefits. It is of paramount importance to identify and filter out fake news especially in democratic societies. Most existing methods for detecting fake news involve traditional supervised machine learning which has been quite ineffective. In this paper, we are analyzing word embedding features that can tell apart fake news from true news. We use the LIAR and ISOT data set. We churn out highly correlated news data from the entire data set by using cosine similarity and other such metrices, in order to distinguish their domains based on central topics. We then employ auto-encoders to detect and differentiate between true and fake news while also exploring their separability through network analysis.

Rethinking public health using behavioural science

2017 ◽  
Vol 1 (9) ◽  
pp. 612-612 ◽  
Author(s):  
Michael Hallsworth
Keyword(s):  
Public Health ◽  
Behavioural Science
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