Citation practices and the construction of scientific fact. Presentation at the European Conference on Argumentation preconference on status, relevance, and authority of facts.
Citation practices and the construction of scientific fact--ECA-facts-preconference--2017-06-19
1. Citation practices and the
construction of scientific fact
Jodi Schneider
European Conference on Argumentation preconference: status, relevance, and
authority of facts
Fribourg, Switzerland
2017-06-19
jschneider@pobox.com
http://jodischneider.com/jodi.html
@jschneider
2. “[Y]ou can transform a fact into
fiction or a fiction into fact just by
adding or subtracting references”
- Bruno Latour
4. ... two miRNAs, miRNA-372 and-373, function as potential novel oncogenes in
testicular germ cell tumors by inhibition of LATS2 expression, which suggests that
Lats2 is an important tumor suppressor (Voorhoeve et al., 2006).
Raver-Shapira et.al, JMolCell 2007
miR-372 and miR-373 target the Lats2 tumor suppressor (Voorhoeve et al., 2006)
Yabuta, JBioChem 2007:
As claims get cited, they become facts:
To investigate the possibility that miR-372 and miR-373 suppress the
expression of LATS2, we...
Therefore, these results point to LATS2 as a mediator of the miR-372 and miR-373
effects on cell proliferation and tumorigenicity,
Voorhoeve et al, Cell, 2006:
Hypothesis
Implication
Cited Implication
Fact
Slide credit: Anita DeWaard: 'Stories that persuade with data' - talk at CENDI meeting January 9 2014
https://www.slideshare.net/anitawaard/stories-that-persuade-with-data-talk-at-cendi-meeting-january-
9-2014/6
5. Miscitation & bad assumptions
“False claims regarding a causal link between
game playing and obesity have propagated in the
literature on exertion games.”
“While the causal link between game play and
obesity is not supported by evidence from health
research, a version of this argument is presented
many times in published exertion game literature,
complete with supporting citations from public
health research.”
Marshall, J., & Linehan, C. (2017). Misrepresentation of health research in
exertion games literature. In Proceedings of the 2017 CHI Conference on
Human Factors in Computing Systems (pp. 4899-4910). ACM.
6. Miscitation & bad assumptions
“False claims regarding a causal link between
game playing and obesity have propagated in the
literature on exertion games.”
“While the causal link between game play and
obesity is not supported by evidence from health
research, a version of this argument is presented
many times in published exertion game literature,
complete with supporting citations from public
health research.”
Marshall, J., & Linehan, C. (2017). Misrepresentation of health research in
exertion games literature. In Proceedings of the 2017 CHI Conference on
Human Factors in Computing Systems (pp. 4899-4910). ACM.
7. How well do we cite?
Haussmann, N. S., McIntyre, T., Bumby, A. J., & Loubser, M. J. (2013).
Referencing practices in physical geography: how well do we cite what we
write?. Progress in Physical Geography, 37(4), 543-549.
8. How might miscitation happen?
• Reading errors
• Hasty literature reviews
• Reviewer errors
• Cherry picking to justify pre-existing agenda
Marshall, J., & Linehan, C. (2017). Misrepresentation of health research in
exertion games literature. In Proceedings of the 2017 CHI Conference on
Human Factors in Computing Systems (pp. 4899-4910). ACM.
9. Citing fake science harms people
A paper about a clinical trial for renal disease was retracted because:
“‘the trial had not been approved by the ethics committee, the involvement of a
statistician could not be verified, [and] the trial was not a double-blind study,
because Dr Nakao knew the treatment allocation’.”
“Nevertheless, the COOPERATE study was cited by 173 review articles and
58 secondary clinical studies that enrolled a total of 35,929 patients.”
“The harm done by COOPERATE is thus 4-fold:
• patients were enrolled in an experimental therapy for a condition which
already had an accepted therapy;
• time, energy and money were wasted by patients and investigators;
• false information pervaded the literature;
• and combination therapy was accepted more quickly and used more widely
than it might have been otherwise.”
Steen, R. G. (2011). Retractions in the medical literature: how many
patients are put at risk by flawed research?. Journal of Medical
Ethics, 37(11), 688-692.
10. Citing fake science harms people
A paper about a clinical trial for renal disease was retracted because:
“‘the trial had not been approved by the ethics committee, the involvement of a
statistician could not be verified, [and] the trial was not a double-blind study,
because Dr Nakao knew the treatment allocation’.”
“Nevertheless, the COOPERATE study was cited by 173 review articles and
58 secondary clinical studies that enrolled a total of 35,929 patients.”
“The harm done by COOPERATE is thus 4-fold:
• patients were enrolled in an experimental therapy for a condition which
already had an accepted therapy;
• time, energy and money were wasted by patients and investigators;
• false information pervaded the literature;
• and combination therapy was accepted more quickly and used more widely
than it might have been otherwise.”
Steen, R. G. (2011). Retractions in the medical literature: how many
patients are put at risk by flawed research?. Journal of Medical
Ethics, 37(11), 688-692.
11. “The conversion of hypothesis to
fact through citation alone.”
- Stephen Greenberg
12. Greenberg, Steven A.
"Understanding belief using
citation networks." Journal of
evaluation in clinical
practice 17.2 (2011): 389-393.
http://dx.doi.org/
10.1111/j.1365-
2753.2011.01646.x
13. “The conversion of hypothesis to fact
through citation alone.”
- Stephen Greenberg
Greenberg, Steven A. "How citation distortions create unfounded
authority: analysis of a citation network." BMJ 339 (2009): b2680.
https://doi.org/10.1136/bmj.b2680
14. Funded grants with citation bias &
citation distortion.
Greenberg, Steven A. "How citation distortions create unfounded
authority: analysis of a citation network." BMJ 339 (2009): b2680.
https://doi.org/10.1136/bmj.b2680
16. Boyce, R.D.: A Draft Evidence Taxonomy and Inclusion Criteria for the
Drug Interaction Knowledge Base (DIKB),
http://purl.net/net/drug-interaction-knowledge-base/evidence-types-and-
inclusion-criteria
Ask: What evidence is relevant
for a given purpose?
17. Ask: What evidence is strong
enough?
Figure credit: SUNY Downstate Medical Center. Medical Research
Library of Brooklyn. Evidence Based Medicine Course. A Guide to
Research Methods: The Evidence Pyramid:
http://library.downstate.edu/EBM2/2100.htm
18. Ask: Can we
model scientific
arguments and
evidence?
Clark, Tim, Paolo N. Ciccarese, and Carole A. Goble.
"Micropublications: a semantic model for claims, evidence, arguments
and annotations in biomedical communications." Journal of Biomedical
Semantics 5.28 (2014). http://dx.doi.org/10.1186/2041-1480-5-28
19. Jodi Schneider, Paolo Ciccarese, Tim Clark, Richard D. Boyce. “Using the Micropublications ontology and the
Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base.” Linked
Science at ISWC 2014 http://ceur-ws.org/Vol-1282/lisc2014_submission_8.pdf
22. SEPIO – evidence lines
Brush, Matthew, Kent Shefchek, and Melissa Haendel. "SEPIO: a
semantic model for the integration and analysis of scientific
evidence." International Conference on Biomedical Ontology and
BioCreative. 2016. http://ceur-ws.org/Vol-1747/IT605_ICBO2016.pdf
“A proposition has_evidence
one or more evidence lines, which have_supporting_data
one or more data items used in evaluation of the
proposition’s truth.”
23. SEPIO – evidence lines example
Brush, Matthew, Kent Shefchek, and Melissa Haendel. "SEPIO: a
semantic model for the integration and analysis of scientific
evidence." International Conference on Biomedical Ontology and
BioCreative. 2016. http://ceur-ws.org/Vol-1747/IT605_ICBO2016.pdf
“A simplified account of existing evidence related to this proposition is presented below,
presenting summaries of five evidence lines (E1-E5) from five studies relevant to the
classification of the variant for Fabry Disease:
E1. Six affected individuals with the variant were found to have reduced GLA enzyme
activity.
E2. The variant was absent from 528 unaffected controls.
E3. The variant is predicted to cause abnormal splicing that inserts additional sequence.
E4. Pedigree analyses showed Fabry Disease phenotypes segregating with the variant.
E5. Population databases show high frequency of individuals homozygous for the variant.”
24. SEPIO – evidence lines example
Brush, Matthew, Kent Shefchek, and Melissa Haendel. "SEPIO: a
semantic model for the integration and analysis of scientific
evidence." International Conference on Biomedical Ontology and
BioCreative. 2016. http://ceur-ws.org/Vol-1747/IT605_ICBO2016.pdf
“A simplified account of existing evidence related to this proposition is presented below,
presenting summaries of five evidence lines (E1-E5) from five studies relevant to the
classification of the variant for Fabry Disease:
E1. Six affected individuals with the variant were found to have reduced GLA enzyme
activity.
E2. The variant was absent from 528 unaffected controls.
E3. The variant is predicted to cause abnormal splicing that inserts additional sequence.
E4. Pedigree analyses showed Fabry Disease phenotypes segregating with the variant.
E5. Population databases show high frequency of individuals homozygous for the variant.”
26. SEE
Bö̈ lling, Christian, Michael Weidlich, and Hermann-Georg Holzhütter.
"SEE: structured representation of scientific evidence in the biomedical
domain using Semantic Web techniques." Journal of Biomedical
Semantics 5.1 (2014): 1.
27. SEE
Bö̈ lling, Christian, Michael Weidlich, and Hermann-Georg Holzhütter.
"SEE: structured representation of scientific evidence in the biomedical
domain using Semantic Web techniques." Journal of Biomedical
Semantics 5.1 (2014): 1.
28. Ask: Can we structure the
arguments
• “An ontology is a formal, explicit
specification of a shared conceptualisation.”
- (Gruber, 1993)
• Make clear the lines of argument
• Enable formal reasoning (OWL reasoners)
Gruber, Thomas R. "Toward principles for the design of ontologies used
for knowledge sharing?." International journal of human-computer
studies 43.5-6 (1995): 907-928. DOI:10.1006/ijhc.1995.1081
29. Ask: Should the evidence be
aggregated?
Figure credit: Forest plot from Underhill, Kristen, Paul
Montgomery, and Don Operario. "Sexual abstinence only
programmes to prevent HIV infection in high income countries:
systematic review." BMJ 335.7613 (2007): 248.
30. Figure credit: Duke University Medical Center Library. Introduction to
Evidence-based Practice. What is Evidence-Based Practice (EBP)?
http://guides.mclibrary.duke.edu/c.php?g=158201&p=1036021
Contextualize evidence with
values and expertise
Editor's Notes
Greenberg, Steven A. "How citation distortions create unfounded authority: analysis of a citation network." BMJ 339 (2009): b2680. https://doi.org/10.1136/bmj.b2680
Latour, Bruno. Science in action: How to follow scientists and engineers through society. Harvard University Press, 1987. p33
The COOPERATE study was a falsified clinical trial on 263 patients with non-diabetic renal disease.
The COOPERATE study was a falsified clinical trial on 263 patients with non-diabetic renal disease.
Greenberg, Steven A. "How citation distortions create unfounded authority: analysis of a citation network." BMJ 339 (2009): b2680. https://doi.org/10.1136/bmj.b2680
Latour, Bruno. Science in action: How to follow scientists and engineers through society. Harvard University Press, 1987. p33
“As is commonly the case, different evidence is used by each lab - either because certain data were not accessible, or some labs judged certain data to be unreliable or irrelevant to the claim, or some labs interpreted the same data in different ways. SEPIO translates this scenario into the following narrative and set of instances to be represented in its formal modeling of the data.”
“As is commonly the case, different evidence is used by each lab - either because certain data were not accessible, or some labs judged certain data to be unreliable or irrelevant to the claim, or some labs interpreted the same data in different ways. SEPIO translates this scenario into the following narrative and set of instances to be represented in its formal modeling of the data.”
“A model of the evidence for and against the assertion escitalopram does not inhibit CYP2D6. This is based on the Micropublications ontology, and reuses the ev- idence taxonomy (dikbEvidence), terms (dikb), and data from the DIKB. The Drug Ontology (DRON) and Protein Ontology (PRO) are reused in semantic qualifiers. A more detailed view of Method Me1 is shown in Figure 1. "
“As is commonly the case, different evidence is used by each lab - either because certain data were not accessible, or some labs judged certain data to be unreliable or irrelevant to the claim, or some labs interpreted the same data in different ways. SEPIO translates this scenario into the following narrative and set of instances to be represented in its formal modeling of the data.”
“As is commonly the case, different evidence is used by each lab - either because certain data were not accessible, or some labs judged certain data to be unreliable or irrelevant to the claim, or some labs interpreted the same data in different ways. SEPIO translates this scenario into the following narrative and set of instances to be represented in its formal modeling of the data.”
“As is commonly the case, different evidence is used by each lab - either because certain data were not accessible, or some labs judged certain data to be unreliable or irrelevant to the claim, or some labs interpreted the same data in different ways. SEPIO translates this scenario into the following narrative and set of instances to be represented in its formal modeling of the data.”
“As is commonly the case, different evidence is used by each lab - either because certain data were not accessible, or some labs judged certain data to be unreliable or irrelevant to the claim, or some labs interpreted the same data in different ways. SEPIO translates this scenario into the following narrative and set of instances to be represented in its formal modeling of the data.”
“As is commonly the case, different evidence is used by each lab - either because certain data were not accessible, or some labs judged certain data to be unreliable or irrelevant to the claim, or some labs interpreted the same data in different ways. SEPIO translates this scenario into the following narrative and set of instances to be represented in its formal modeling of the data.”
Gruber, Thomas R. "Toward principles for the design of ontologies used for knowledge sharing?." International journal of human-computer studies 43.5-6 (1995): 907-928.
Is Wikipedia a gateway to biomedical research? (Lauren Maggio)
![“[Y]ou can transform a fact into
fiction or a fiction into fact just by
adding or subtracting references”
- Bruno Latour](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)