De volgende vragen gaan over de bestrijding (curatief vs. Preventief) van de Plastic Soup kwestie.
C1 Bestrijden - Preventief
Definitie: Preventie van Plastic Soup betreffen alle maatregelen die worden genomen om Plastic
Soup te voorkomen, zoals het invoeren van nieuwe wetten die plastic vervuiling tegengaan. Denk hierbij aan het spreekwoord: “Beter voorkomen dan genezen”.
0 = Nee 1 = Ja
C2 Bestrijden - Curatief
Wordt enige vorm van curatieve bestrijding van de Plastic Soup kwestie genoemd?
Definitie: Curatief bestrijden heeft te maken met het “genezen” van schade door Plastic Soup dat
zich al heeft voorgedaan, zoals: het opruimen van plastic op stranden, in de stad, ed. Denk hierbij aan het spreekwoord: “Beter voorkomen dan genezen”.
0 = Nee 1 = Ja
D. Attributen
De volgende vragen gaan over specifieke attributen van de Plastic Soup kwestie.
D1 BTW
Wordt in het specifiek BTW benoemd in relatie tot de Plastic Soup kwestie?
Definitie: Onder BTW worden alle onderwerpen in relatie tot belastingheffing op goederen en
diensten gerekend.
0 = Nee 1 = Ja
D2 Dierenwelzijn
Wordt in het specifiek dierenwelzijn benoemd in relatie tot de Plastic Soup kwestie?
Definitie: Onder dierenwelzijn vallen alle onderwerpen die betrekking hebben op het welzijn van
dieren (landdieren, vissen en vogels), zoals onder andere dierenleed.
1 = Ja
D3 Digitalisering & Innovatie
Wordt in het specifiek digitalisering en/of innovatie benoemd in relatie tot de Plastic Soup kwestie?
Definitie: Onder digitalisering & innovatie wordt gerekend: alle onderwerpen waarin innovatieve,
digitale methoden worden besproken die bijdragen aan het bestrijden van Plastic Soup, zoals apps om zwerfafval op te ruimen of het gebruik van big data om plasticvervuiling van de oceanen in kaart te brengen.
0 = Nee 1 = Ja
D4 Donaties
Wordt in het specifiek donaties benoemd in relatie tot de Plastic Soup kwestie?
Definitie: Onder donaties wordt gerekend: alle berichtgeving waarin gesproken wordt over donaties
(geld, goederen of diensten) aan kwetsbare groepen en/of NGOs en goede doelen.
0 = Nee 1 = Ja
D5 Gezondheid - Drinkwatervervuiling
Wordt in het specifiek drinkwatervervuiling benoemd in relatie tot de Plastic Soup kwestie?
Definitie: Onder drinkwatervervuiling wordt gerekend: alle berichtgeving waarin de vervuiling van
drinkwater (en de invloed daarvan op de menselijke gezondheid) ten gevolge van Plastic Soup wordt besproken.
0 = Nee 1 = Ja
D6 Gezondheid - Luchtkwaliteit
Definitie: Onder luchtkwaliteit wordt gerekend: alle berichtgeving waarin de vervuiling van de lucht
(en de invloed daarvan op de menselijke gezondheid), onder andere door CO2-uitstoot, ten gevolge van Plastic Soup wordt besproken.
0 = Nee 1 = Ja
D7 Gezondheid - Voedselverontreiniging
Wordt in het specifiek voedselverontreiniging benoemd in relatie tot de Plastic Soup kwestie benoemd?
Definitie: Onder voedselverontreiniging wordt gerekend: alle berichtgeving waarin de vervuiling van
voedsel (en de invloed daarvan op de menselijke gezondheid), zoals besmetting van voedsel met microplastics of biochemicals, ten gevolge van Plastic Soup wordt besproken.
0 = Nee 1 = Ja
D8 Gezondheid - Algemeen
Wordt in het specifiek algemene gezondheid benoemd in relatie tot de Plastic Soup kwestie benoemd?
Definitie: Onder algemene gezondheid wordt alle overige berichtgeving gerekend waarin de invloed
van Plastic Soup op de menselijke gezondheid wordt besproken, exclusief berichtgeving waarin specifiek gesproken wordt over drinkwater- en voedselvervuiling en luchtkwaliteit.
0 = Nee 1 = Ja
D9 Onderzoek & Informatievoorziening
Wordt in het specifiek onderzoek aangehaald of informatie voorzien in relatie tot de Plastic Soup kwestie benoemd?
Definitie: Onder onderzoek & informatievoorziening wordt gerekend: alle berichtgeving waarin (de
resultaten van) onderzoek naar Plastic Soup wordt aangehaald, zoals kerncijfers en andere kennisgeving.
1 = Ja
D10 Statiegeld
Wordt in het specifiek statiegeld in relatie tot de Plastic Soup kwestie benoemd?
Definitie: Onder statiegeld wordt gerekend: alle berichtgeving waarin statiegeld wordt genoemd in
relatie tot Plastic Soup, zoals de nieuwe regelgeving waarbij statiegeldheffing wordt uitgebreid naar flesjes en blikjes.
0 = Nee 1 = Ja
D11 Onderwijs
Wordt het onderwijs specifiek benoemd in relatie tot de Plastic Soup kwestie?
Definitie: Onder onderwijs wordt gerekend: alle berichtgeving waarin onderwijs(instellingen) en
opleidingen in relatie tot Plastic Soup worden genoemd, zoals de introductie van speciale lespakketten voor scholieren over plastic vervuiling.
0 = Nee 1 = Ja
D11 Wateren
Worden specifieke wateren benoemd in relatie tot de Plastic Soup kwestie?
Definitie: Onder wateren worden specifieke oceanen (e.g., Atlantische Oceaan), zeeën (e.g.,
Waddenzee, Noordzee), rivieren (e.g., de Maas, de Rijn, de Nijl) of andere wateren (e.g., grachten, meren) gerekend.
0 = Nee 1 = Ja
Worden specifieke plastic producten en/of microplastics benoemd in relatie tot de Plastic Soup kwestie?
Definitie: Onder plastic producten en/of microplastics wordt gerekend: alle berichtgeving waarin
over specifieke, vervuilende producten wordt gesproken in relatie tot Plastic Soup, zoals: microplastics (kleine plastic deeltjes in o.a. kleding), ballonnen, confetti, rietjes, sigaretten, verpakkingen en tasjes (drinkflesjes en -blikjes, voedselverpakkingen, zakjes), wattenstaafjes of visnetten.
0 = Nee 1 = Ja
D13 Duurzaamheid & Mileu
Worden specifiek de (werk)woorden duurzaamheid, klimaat, landschap, milieu en/of natuur(vervuiling) genoemd in relatie tot de Plastic Soup kwestie?
0 = Nee 1 = Ja
D14 Zwerfafval
Worden specifiek de zelfstandig naamwoorden zwerfafval en/of zwerfvuil genoemd in relatie tot de Plastic Soup kwestie?
0 = Nee 1 = Ja
D15 Oorzaak Plastic Soup
Wordt in het specifiek het werkwoord veroorzaken (van de Plastic Soup kwestie) genoemd?
0 = Nee 1 = Ja
Appendix F
R-code for constructing agenda network matrices and network visualizations
#load needed packages library(statnet) library(tidyverse) library(dplyr) library(igraph) library(dils) library(tidygraph) library(ggraph) library(RColorBrewer) library(wesanderson) library(knitr)
#Preparing an unweighted sociomatrix (adjacency matrix) with accompanying node-level covariates for analysis.
edge_list <- tibble(from = c("A", "A", "A", "B", "B"), to = c("B", "C", "D","C", "D"), weight = c(1, 2, 3, 4, 5))
node_list <- tibble(id = 1:4) #Create node list
nodes <- tibble(ID = c("A","B","C","D","E","F","G","H","I","J","K","L","M","N"), labels = c("Fighting plastic pollution", "VAT", "Animal welfare", "Digitization & innovation", "Donations", "Research & information provision", "Deposit money", "Waters", "Education", "The causation of plastic pollution", "Sustainability & environmental impact", "Health", "Specific (plastic) products", "Stakeholder involvement"))
#Create edge list for NEWS MEDIA edge_list_media <- tibble(from =
c("A","A","A","A","A","A","A","A","A","A","A","A","A","B","B","B","B","B","B","B","B","B","B ","B","B", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "E", "E", "E", "E", "E", "E", "E", "E", "E","F","F","F","F","F","F","F","F", "G", "G", "G", "G", "G", "G", "G", "H", "H", "H", "H", "H", "H", "I", "I", "I", "I", "I", "J", "J", "J", "J", "K", "K", "K", "L", "L", "M"), to = c("B", "C", "D", "E","F", "G", "H", "I", "J", "K", "L", "M", "N", "C", "D", "E","F", "G", "H", "I", "J", "K", "L", "M", "N", "D", "E","F", "G", "H", "I", "J", "K", "L", "M", "N", "E","F", "G", "H", "I", "J", "K", "L", "M", "N","F", "G", "H", "I", "J", "K", "L", "M", "N", "G", "H", "I", "J", "K", "L", "M", "N", "H", "I", "J", "K", "L", "M", "N", "I", "J", "K", "L", "M", "N", "J", "K", "L", "M", "N", "K", "L", "M", "N", "L", "M", "N", "M", "N", "N"), weight =
c(0,10,8,1,9,72,89,1,258,72,13,120,172,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,2,12,1,0,3,4,5,21,0,1,0,2,0,0,0,0,6 ,7,0,1,0,0,0,0,0,0,1,2,9,3,0,6,1,5,11,23,0,0,9,5,57,65,1,0,32,25,49,112,0,2,0,1,2,0,0,0,0,11,25,51,19,36, 116))
Media_graph <- graph.data.frame(edge_list_media,directed=FALSE); Media_matrix <-
as_adjacency_matrix(Media_graph,type="both",names=TRUE,sparse=FALSE,attr="weight") diag(Media_matrix) <- 0
#Visualisatie NEWS MEDIA
media_igraph_tidy <- as_tbl_graph(media_igraph) class(media_igraph_tidy)
media_igraph_tidy %>%
mutate(centrality = centrality_authority()) %>% ggraph(layout = "nicely") +
geom_edge_link(aes(width = weight), alpha = 0.8, show.legend = FALSE) + scale_edge_width(range = c(0.2, 2)) +
geom_node_point(aes(colour = labels, size = centrality)) + guides(size=FALSE) +
labs(colour = "Issue (attributes)") +
scale_color_manual(values=c("#FF3399", "#CC33CC", "#993399","#663399", "#6600CC", "#9966FF","#0033FF", "#33CCFF", "#003399","#006666", "#009999", "#00CC33","#CCFF00", "#FFFF00")) +
theme_graph(base_family = "Times New Roman", base_size = 13)
ggsave("media_network_1.png", plot = last_plot(), device = NULL, path = NULL, scale = 1, width = 10, height = 5)
#Create edge list for NGOs edge_list_ngo <- tibble(from =
c("A","A","A","A","A","A","A","A","A","A","A","A","A","B","B","B","B","B","B","B","B","B","B ","B","B", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "E", "E", "E", "E", "E", "E", "E", "E", "E","F","F","F","F","F","F","F","F", "G", "G", "G", "G", "G", "G", "G", "H", "H", "H", "H", "H", "H", "I", "I", "I", "I", "I", "J", "J", "J", "J", "K", "K", "K", "L", "L", "M"), to = c("B", "C", "D", "E","F", "G", "H", "I", "J", "K", "L", "M", "N", "C", "D", "E","F", "G", "H", "I", "J", "K", "L", "M", "N", "D", "E","F", "G", "H", "I", "J", "K", "L", "M", "N", "E","F", "G", "H", "I", "J", "K", "L", "M", "N","F", "G", "H", "I", "J", "K", "L", "M", "N", "G", "H", "I", "J", "K", "L", "M", "N", "H", "I", "J", "K", "L", "M", "N", "I", "J", "K", "L", "M", "N", "J", "K", "L", "M", "N", "K", "L", "M", "N", "L", "M", "N", "M", "N", "N"), weight =
c(0,14,24,4,44,274,138,2,1,84,10,286,355,0,0,0,0,0,0,0,0,1,0,0,1,1,0,11,12,10,5,1,2,5,17,24,0,5,0,1,0, 0,2,0,20,23,0,2,4,0,0,1,0,2,14,22,55,18,0,17,0,47,57,88,1,1,69,4,191,236,4,1,59,6,111,207,0,1,0,2,5,0, 0,2,2,1,59,132,14,24,289))
#Create adjacency matrix for NGOs
NGO_graph <- graph.data.frame(edge_list_ngo,directed=FALSE); NGO_matrix <-
as_adjacency_matrix(NGO_graph,type="both",names=TRUE,sparse=FALSE,attr="weight") diag(NGO_matrix) <- 0
#Create igraph for NGOs
NGO_igraph <- graph_from_data_frame(d = edge_list_ngo, vertices = nodes, directed = FALSE) #Visualisatie NGOs
NGO_igraph_tidy <- as_tbl_graph(NGO_igraph) class(NGO_igraph_tidy)
NGO_igraph_tidy %>%
mutate(centrality = centrality_authority()) %>% ggraph(layout = "nicely") +
geom_edge_link(aes(width = weight), alpha = 0.8, show.legend = FALSE) + scale_edge_width(range = c(0.2, 2)) +
geom_node_point(aes(colour = labels, size = centrality)) + guides(size=FALSE) +
labs(colour = "Issue (attributes)") +
scale_color_manual(values=c("#FF3399", "#CC33CC", "#993399","#663399", "#6600CC", "#9966FF","#0033FF", "#33CCFF", "#003399","#006666", "#009999", "#00CC33","#CCFF00", "#FFFF00")) +
theme_graph(base_family = "Times New Roman", base_size = 13)
ggsave("ngo_network_1.png", plot = last_plot(), device = NULL, path = NULL, scale = 1, width = 10, height = 5)
#Create edge list for THE PUBLIC edge_list_public <- tibble(from =
c("A","A","A","A","A","A","A","A","A","A","A","A","A","B","B","B","B","B","B","B","B","B","B ","B","B", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "E", "E", "E", "E", "E", "E", "E", "E", "E","F","F","F","F","F","F","F","F", "G", "G", "G", "G", "G", "G", "G", "H", "H", "H", "H", "H", "H", "I", "I", "I", "I", "I", "J", "J", "J", "J", "K", "K", "K", "L", "L", "M"), to = c("B", "C", "D", "E","F", "G", "H", "I", "J", "K", "L", "M", "N", "C", "D", "E","F", "G", "H", "I", "J", "K", "L", "M", "N", "D", "E","F", "G", "H", "I", "J", "K", "L", "M", "N", "E","F", "G", "H", "I", "J", "K", "L", "M", "N","F", "G", "H", "I", "J", "K", "L", "M", "N", "G", "H", "I", "J", "K", "L", "M", "N", "H", "I", "J", "K", "L", "M", "N", "I", "J", "K", "L", "M", "N", "J", "K", "L", "M", "N", "K", "L", "M", "N", "L", "M", "N", "M", "N", "N"), weight =
c(9,279,520,79,366,5289,3373,83,31,1953,734,7725,9265,0,0,0,0,12,25,0,0,48,6,12,89,9,1,124,141,6 72,109,10,156,423,394,1194,0,37,36,67,5,5,34,11,464,376,1,57,28,1,0,51,0,70,147,173,404,243,3,136 ,3,360,736,1168,29,19,870,164,3988,4702,76,40,1549,1257,2786,5500,0,43,2,88,250,42,6,73,71,549, 2086,2901,1235,1417,7990))
#Create adjacency matrix for THE PUBLIC
public_graph <- graph.data.frame(edge_list_public,directed=FALSE); public_matrix <-
as_adjacency_matrix(public_graph,type="both",names=TRUE,sparse=FALSE,attr="weight") diag(public_matrix) <- 0
#Create igraph for THE PUBLIC
public_igraph <- graph_from_data_frame(d = edge_list_public, vertices = nodes, directed = FALSE) #Visualisatie THE PUBLIC
public_igraph_tidy <- as_tbl_graph(public_igraph) class(public_igraph_tidy)
public_igraph_tidy %>%
mutate(centrality = centrality_authority()) %>% ggraph(layout = "nicely") +
geom_edge_link(aes(width = weight), alpha = 0.8, show.legend = FALSE) + scale_edge_width(range = c(0.2, 2)) +
geom_node_point(aes(colour = labels, size = centrality)) + guides(size=FALSE) +
labs(colour = "Issue (attributes)") +
scale_color_manual(values=c("#FF3399", "#CC33CC", "#993399","#663399", "#6600CC", "#9966FF","#0033FF", "#33CCFF", "#003399","#006666", "#009999", "#00CC33","#CCFF00", "#FFFF00")) +
theme_graph(base_family = "Times New Roman", base_size = 13)
ggsave("public_network_1.png", plot = last_plot(), device = NULL, path = NULL, scale = 1, width = 10, height = 5)