29 mars 2020
The new norm in research will be about QUALITY NOT QUANTITY ! #PublishorPerish culture is harming academia! #Merton_norms to Make Academia Great Again ;)
In 1942, sociologist Robert Merton articulated an ethos of science in “A Note on Science and Technology in a Democratic Order.” He argued that, although no formal scientific code exists, the values and norms of modern science can nevertheless be inferred from scientists’ common practices and widely held attitudes. Merton discussed four idealized norms: Universalism, Communality, Disinterestedness, and Organized Skepticism. In this video, we explore what these norms are and what they mean for the scientific community. « A Note on Science and Technology » can be found at: http://www.collier.sts.vt.edu/5424/pd…
Merton, Robert K. 1973. The Sociology of Science: Theoretical and Empirical Investigations. University of Chicago Press.
Source: Berkeley Initiative for Transparency in the Social Sciences (BITSS)
25 mars 2020
The GIGA Failure! Fiat currencies may be gone by end of this year, Alasdair Macleod tells Keiser Report
25 mars 2020
Corrupt Research: The Case for Reconceptualizing Empirical Management and Social Science
Hubbard, R. (2015). Corrupt research: The case for reconceptualizing empirical management and social science. Sage Publications.
I’m reading this great book written by Hubbard a great marketing scholar about the current NHST paradigm in research. The author is defending the idea that we are producing « unreliable science » that is absolutely useless! Summarizes lots of evidence that the pervasive use of NHTS (Null Hypothesis statistical testing) is bad practice. The author suggests another model and defends the same ideas of Geoff Cumming about the new statistics based on confidence intervals instead of p values and open science in his book of 2013 Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. Routledge.
The book of Hubbard is a major contribution and alert toward the research community about the actual research paradigm that we can qualify as an « Ostrich politic » where publishers and academics waste ressources (time, money, effort) for advancing their careers through « fake or misleading » publications instead of seeking to change the system and to seek the truth! this is the noble objective of science, not citations, or rankings! We need a cumulative system that allows replication and integration of findings.
As expected no one is a prophet is his own country, like any established community, gatekeepers don’t like people who go against the mainstream doxa, and this book received little interest while it’s a major contribution and very urgent issue in academic research. Few are those who have the courage and honesty to say it loudly like the excellent positions taken by Barnett , or Kuntz , or Cumming ! but history will give credit to those who stood with a backbone for the truth.
I follow many groups in various disciplines, and this issue will be the TRUE CHALLENGE for the next years if we want to tackle the credibility of scientific research! Corrupt academic research has to change! The publish or perish system is a very bad model to copy! Thus, the present rankings of universities and what we believe about real scholarship is again another example of falsehood and a corrupt order. I always said that publicly, this scientific model is not honest and it will collapse if it continues to manufactures meaningless paper!
YB
This great video by Dr. Roland GORI reminds us with many examples that this order is not sustainable nor honest. Subtitles are available in English.
5 octobre 2019
Dance of the p Values & reporting intevals by Geoff Cumming
Cumming, Geoff. Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. Routledge, 2013.
16 juillet 2019
Nature is a big library that we should learn to observe and mimic ! Copying nature is being at the edge of high tech. #Biomimicry_design
16 juillet 2019
Introduction to LCA : Latent Class Analysis #Marketingthema
Latent class analysis (LCA) identifies unobservable subgroups within a population. We work to expand LCA models to allow scientists to better understand the impact of exposure to patterns of multiple risks, as well as the antecedents and consequences of complex behaviors, so that interventions can be tailored to target the subgroups that will benefit most.
Some statistical packages able to handle this analysis:
- Latent GOLD
- Mplus
- LEM (Free)
8 juillet 2019
Watch French research in real-time ! Live consultation of articles from several full-text electronic scientific journals and databases.
8 juillet 2019
Visualizing literature review using #VosViewer | Discover new links between authors, topics, keywords, domains, etc.
11 juin 2019
JASP 0.10 has been released #JASP
See the list here for all the analyses currently available in JASP. To find out how to perform certain analyses or how to use certain features.
28 mai 2019
Interesting paper on how to identify potential mediators in ‘high-dimensional’ datasets
This paper is very interesting because i believe the future will be more inductive than deductive, datasets, will tell us the theory on how the world works through mining large datasets. The author uses an original method to identify the variables (columns) that have an impact on the dependent variable, without having the limitations related to the methods of structural equations that require a number of observations greater than the number of variables (sometimes the threshold recommended is 10 observations per variable in the file). This method is therefore an original contribution to understand how to look for mediators by making the data talk without any a priori!
Van Kesteren, E. J., & Oberski, D. L. (2019). Exploratory Mediation Analysis with Many Potential Mediators
The paper is available here : read
14 mai 2019
Death of the author? AI generated books and the production of scientific knowledge
Artificial Intelligence (AI) has been applied to an increasing number of creative tasks from the composition of music, to painting and more recently the creation of academic texts. Reflecting on this development Harry Collins, considers how we might understand AI in the context of academic writing and warns that we should not confuse the work of algorithms with tacit complex socially constructed forms of knowledge.
Apparently there are now academic books generated by artificial intelligence algorithms. An example just published by Springer Nature, and written by ‘Beta Writer’, is called Lithium-Ion Batteries: A Machine-Generated Summary of Current Research. I don’t know anything much about Lithium-Ion batteries, nor about how these algorithms work, but I do know something about scientific knowledge and the way it is generated. I have also written three books (without the aid of an algorithm) on artificial intelligence that draw on this knowledge, most recently: Artifictional Intelligence, Against humanity’s surrender to computers.
15 avril 2019
Google offers a new search engine to find #datasets on specific topics
Google offers a new search engine to find datasets on specific topics. Here is the link:
https://toolbox.google.com/datasetsearch
This could be a great tool to train students on data, or even conduct research on secondary useful datasets.
« In today’s world, scientists in many disciplines and a growing number of journalists live and breathe data. There are many thousands of data repositories on the web, providing access to millions of datasets; and local and national governments around the world publish their data as well. To enable easy access to this data, we launched Dataset Search, so that scientists, data journalists, data geeks, or anyone else can find the data required for their work and their stories, or simply to satisfy their intellectual curiosity. Similar to how Google Scholar works, Dataset Search lets you find datasets wherever they’re hosted, whether it’s a publisher’s site, a digital library, or an author’s personal web page. To create Dataset search, we developed guidelines for dataset providers to describe their data in a way that Google (and other search engines) can better understand the content of their pages. These guidelines include salient information about datasets: who created the dataset, when it was published, how the data was collected, what the terms are for using the data, etc. We then collect and link this information, analyze where different versions of the same dataset might be, and find publications that may be describing or discussing the dataset. Our approach is based on an open standard for describing this information (schema.org) and anybody who publishes data can describe their dataset this way. We encourage dataset providers, large and small, to adopt this common standard so that all datasets are part of this robust ecosystem. »
Source here
