Nature of science in students’ discussions on disagreement between scientists following a narrative about health effects of the Fukushima Daiichi accident

Figures & data

Table 1. Translated extract from original Excel coding sheet, illustrating the sequencing of the narrative and coding of narrative elements and expository incursions into NOS-themes.

Storyline		Narrative element or expository incursion	NOS-framework			Lines in App. A
Sequence	Content		Cons	WS	FRA
4	From the Chernobyl accident scientists learnt that ^131I is released in a nuclear accident, and that there is a correlation between exposure to ^131I and incidence of thyroid cancer.	Event 1	- Empirical - Subjective			3–6
5	Explains dose-response relationships and their significance for determining causality. Explains relative risk and confidence interval.	Expository	- Empirical - Subjective	- Evidential relevance - Statistical analysis of error - Correlation vs. causation- Norms of handling scientific data - Nature of graphs		Not included
6	Conclusion by the research community: Increase of thyroid cancer in Chernobyl area was caused by exposure to high doses of ^131I.	Agency (experts)	- Empirical - Subjective	- Evidential relevance	Social certification and dissemination	8–9
7	Two differences were observed between Chernobyl and Fukushima: (a) Fukushima had only 7% av Chernobyl’s emissions of ^131I and (b) a smaller area was expose. So, lower dose and fewer people exposed!	Event 2	- Empirical - Subjective	- Evidential relevance		10–11
8	A group of experts concluded that no effects of exposure to ^131I would be possible to detect, based on comparisons between ‘baseline liftime risk’ and ‘lifetime risk for Fukushima inhabitants’. But note that it was only estimations, not real data. On that basis, the Japanse government decided not to carry out dosimetric measurements in order to avoid panic. Big mistake.	Agency (government and experts)	- Subjective	- Evidential relevance - Completeness of evidence.- Norms of handling scientific data		12–14
9	But people were upset and frightened, and the decision not to conduct dosimetric measurements decrased the public’s trust in authorities and the government. Protests. ‘The government is trying to mislead us’.	Agency (the public)		- Credibility		14–16

Table 2. The 19 NOS-themes identified in the narrative and whether they had a central role in the narrative, were emphasised at some or a couple of instances, or were present but not specifically emphasised. Cons = Consensus NOS-framework (Lederman, 2006), WS = Whole Science NOS-framework (Allchin, 2011), FRA = Family Resemblance Approach to NOS-framework (Erduran et al., 2019).

Nature of science			Role in narrative
Type	Theme		Central	Emphasised	Present
Cons	empirical		x
Cons	subjective		x
Cons	creative				x
Cons	cultural				x
WS	evidential relevance		x
WS	completeness of evidence		x
WS	alternative explanations		x
WS	peer review		x
WS	nature of graphs			x
WS	correlation vs. causation			x
WS	norms of handling scientific data			x
WS	role of probability in inference				x
WS	statistical analysis of error				x
WS	role of imagination and creative synthesis				x
WS	role of cultural beliefs				x
WS	credibility				x
WS	resolving disagreement				x
FRA	methods and methodological rules		x
FRA	social certification and dissemination		x
Total	19		8	3	8

Table

1	Student 1	‘Can you come up with more examples of areas where researchers disagree?’ [reads Q2 from the sheet]. Corona.
2		[laughter]
3	Student 2	Well, I mean …
4	Student 1	Always …
5	Student 3	… very relevant.
6	Student 2	There are certain things that you usually regard as universal truths, but I mean it’s extremely little that belongs to that zone, pretty much is still ‘up for debate’ [in English] what’s true, what’s not, what can be proven, what cannot, that kind of stuff.

Table 3. Distribution of NOS-themes in the group discussions and total number of groups treating each theme. The themes are sorted by whether they appear in the narrative or not and by the number of groups treating them. The eight central NOS-themes in the narrative (cf. Table 1) are indicated with *. Cons = Consensus NOS-framework (Lederman, 2006), WS = Whole Science NOS-framework (Allchin, 2011), FRA = Family Resemblance Approach to NOS-framework (Erduran et al., 2019).

Nature of science		Narrative	Groups								Groups total
Type	Theme		1	2	3	4	5	6	7	8
Cons*	empirical	x	x	x	x	x	x	x	x	x	8
Cons*	subjective	x	x	x	x	x	x	x	x	x	8
WS*	peer review	x	x	x	x		x	x	x	x	7
FRA*	social certification and dissemination	x	x	x	x		x	x	x	x	7
WS*	alternative explanations	x	x	x		x	x	x	x		6
WS*	evidential relevance	x	x	x		x	x		x		5
WS*	completeness of evidence	x	x	x		x			x		4
WS	role of cultural beliefs	x		x	x	x		x			4
Cons	cultural	x		x	x	x		x			4
WS	credibility	x	x	x		x					3
FRA*	methods and methodoligical rules	x	x				x				2
WS	resolving disagreement	x	x								1
WS	role of probability in inference	x							x		1
WS	statistical analysis of error	x							x		1
WS	social resp. of scientists		x	x	x	x	x		x	x	7
Cons	tentative		x	x	x			x		x	5
WS	credibility of sci journals and media		x		x						2
WS	conceptual change				x			x			2
FRA	scientific ethos		x								1
Cons	creative	x									0
WS	correlation vs. causation	x									0
WS	role of imagination and creative synthesis	x									0
WS	norms of handling scientific data	x									0
WS	nature of graphs	x									0
Themes total	24	19	14	12	10	9	8	9	10	6

Table 4. Distribution of NOS-themes between the four questions handed out to the students as support for discussing the narrative. The themes are sorted by the number of questions in which they appear in the student discussions. The eight central NOS-themes in the narrative (cf. Table 1) are indicated with *. Cons = Consensus NOS-framework (Lederman, 2006), WS = Whole Science NOS-framework (Allchin, 2011), FRA = Family Resemblance Approach to NOS-framework (Erduran et al., 2019).

Nature of science			Number of groups discussing the theme under each question				No. of questions including the theme
Type	Theme		Question no.
			1	2	3	4
Cons*	empirical		4	6	7	4	4
Cons*	subjective		4	7	8	7	4
WS*	completeness of evidence		2	2	1	1	4
Cons	tentative		0	3	4	3	3
WS*	evidential relevance		3	0	2	2	3
WS*	alternative explanations		3	4	3	0	3
WS*	peer review		0	1	1	7	3
WS	resolving disagreement		0	1	1	2	3
FRA*	social certification and dissemination		0	1	2	7	3
WS	credibility		0	1	0	3	2
WS	social responsibility of scientists		0	1	0	7	2
Cons	cultural		0	4	0	0	1
WS	role of probability in inference		0	0	1	0	1
WS	statistical analysis of error		0	0	1	0	1
WS	conceptual change		0	2	0	0	1
WS	role of cultural beliefs		0	4	0	0	1
WS	credibility of sci journals and media		0	0	0	2	1
FRA	scientific ethos		0	0	1	0	1
FRA*	methods and methodological rules		2	0	0	0	1

Table

7	Student 1	If it’s in general there are, you know almost every subject in that case, where there’s research, there researchers may, disagree
8	Student 2	Yeah, especially when there’s something pretty new that you don’t know that much about or if it, well, that it’s like now, corona, there are many, you know, there are many experts who disagree about, well, how you should, or well, which measures to take to decrease, well, to decrease risk of infection cause well, and then get more people to survive and not get infected.

Table

9	Student 1	And they also felt that … that, the results won’t show any direct c- … The biggest difference is that they didn’t have a control. They didn’t have any … They didn’t have any way of comparing their results with … cause, how he did it was only an estimate.
10	Student 2	Yeah
11	Student 1	He looked at, how often there is, outside and then it was, thirty times less. And then he said: ‘Ok then there has to be some, effect’.
12	Student 2	Right.
13	Student 1	Whereas others said that … not exactly it was, it was only about, now we’ve tested this many people in the area so, we see … we’ve got a figure, but we have no control.
14	Student 2	Right.

Table

15	Student 1	Right. And it, well, no, but the whole thing. So, then it might be more important that sort of the scientific process is honoured so that what gets out to the public …
16	Student 2	Yes
17	Student 3	… gets the same status as the bad stuff, yeah.
18	Student 1	That, yeah, no, but no, that the bad stuff doesn’t sort of get out before it is, you know …
19	Student 3	Before it’s proven, yeah.
20	Student 1	… proven and sort of it’s, of course everything doesn’t get proven, but you know, but goes through … [interrupted by the teacher]

Table

21	Student 1	Yes. But those who publish need to … think a little extra.
22	Student 2	Yeah. Maybe more responsibility, yeah well …
23	Student 3	But how can they be more responsible? Cause they, they only present their results. And then what’s done, they can’t control that.
24	Student 1	But … they have to check that …
25	Student 3	Cause they can’t be after everyone. I mean that’s simply impossible.
26	Student 1	No, that’s true.
27	Student 3	It’s only because really, what I think, about research results that people for example only take part of the research that agrees with, your own opinion and then it’s a … misinterpretation of the whole, research result.
28	Student 1	Yes.
29	Student 3	Like that. The researchers can’t be blamed for that.

Table

30	Student 1	So how … how should you act, if you see two researchers who disagree?
31	Student 2	In that case you read about it.
32	Student 1	Right
33	Student 2	And check what you think. And choose that which …
34	Student 3	Yeah, you have to choose yourself who …
35	Student 2	Yeah
36	Student 1	Yes, you can choose for yourself. Or you wait until they agree.
37	Student 3	Yes.
38	Student 1	But you never know, that could take like a hundred years. Uhm …

Lederman, N. G. (2006). Syntax of Nature of Science within inquiry and science instruction. In L. B. Flick & N. G. Lederman (Eds.), Scientific Inquiry and Nature of Science (pp. 301–317). Springer Netherlands.

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Allchin, D. (2011). Evaluating knowledge of the nature of (whole) science. Science Education, 95(3), 518–542. doi:10.1002/sce.20432

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