SoLiXG:Greece’s recovery and resilience plan
Greece's plan documents
summary:
pdf:
https://commission.europa.eu/system/files/2021-11/nrrp_greece_2_0_greek_280721.pdf
Report by Reporters United
The article questions the involvement of the private company Price Waterhouse Cooper (PWC) that worked as a consultant for the Greek government for the development of the plan. They critique the lack of information and clarity regarding the level of their involvement. The authors asked the government (not sure) if the company participated in the final decisions and development of the plan as they didn't have the right and their role was limited in the technical support. The ministry of Digital Governance responded that the work started much earlier before their involvement and that they were only there for support.
In general the article is a critique on the dependence on consultant companies which may deal with exclusions from EU while working with nations. An example is the case of Spain and the company Deloitte which was also offering services to the Spanish oil company Cepsa, supporting its efforts to win grants from the Recovery Fund.
Notes
What does this say for the digital sovereignty and autonomy of the national plans?
Word Frequency
Most frequent words
| GR | EN |
|---|---|
|
('κόστους', 1013) |
('cost', 1013) ('investments', 912) ('Part', 830) ('reforms', 722) ('Recovery', 673) ('(ID:', 638) ('Investment', 622) ('Implementation', 615) ('Plan', 612) ('Resilience', 556) ('development', 497) ('cost', 491) ('Axis', 484) ('Description', 476) ('reforms', 445) ('labour', 437) ('system', 416) ('sector', 409) ('improvement', 371) ('Axis', 355) ('Complementarity', 353) ('data', 334) ('reform', 329) ('Framework', 319) ('Increase', 313) ('services', 313) ('Plan', 313) ('target', 310) ('Greece', 306) ('includes', 306) ('aid', 299) ('Reform', 295) ('Plan', 293) ('upgrade', 285) ('Investment', 285) ('Purchase', 279) ('also', 277) ('total', 275) ('digital', 266) ('In addition,', 265) ('digital', 251) ('investment', 249) ('economy', 248) ('project', 246) ('evaluation', 240) ('projects', 239) ('EU', 237) ('EU', 237) ('objectives', 234) ('services', 232) ('system', 231) ('project', 230) ('creation', 230) ('projects', 229) ('analysis', 225) ('transformation', 224) ('systems', 224) ('Integration', 224) ('related', 222) ('assessment', 218) ('Milestone', 217) ('country', 216) ('reduction', 216) ('promotion', 214) ('target', 211) ('use', 208) ('energy', 208) ('quarter', 207) ('concerns', 205) ('basis', 203) ('development', 201) ('data', 200) ('new', 199) ('management', 198) ('Digital', 197) ('Approach', 197) ('Challenges', 196) ('must', 194) ('skills', 190) ('business', 189) ('tackling', 188) ('analysis', 188) ('related', 184) ('information', 180) ('training', 177) ('policies', 177) ('protection', 177) ('provision', 176) ('Objective', 174) ('transformation', 174) ('market', 173) ('included', 172) ('2020', 170) ('control', 170) ('GDP', 167) ('authorities', 165) ('health', 164) ('expected', 163) ('targets', 161) ('according to', 161) ('Fund', 160) ('education', 160) ('implementation', 160) |
Python script
First run this in the terminal:
pdf2txt -o doc.txt doc.pdf
import re
from wordfreq import word_frequency
#this is a script to find the most frequent words in a textfile
lines = open('gr-policy.txt', 'r')
text=lines.read()
text_list=text.replace('\n', ' ').split(".")
lines.close()
sep_words=[]
new_list=[]
all_freq={}
frequency={}
with open("output.txt", "a") as f:
for l in text_list:
for w in l.split():
sep_words.append(w)
for word in sep_words:
freq = sep_words.count(word)
frequency={word:freq}
all_freq.update(frequency)
# all_freq.append(frequency)
new_list=sorted(all_freq.items(), key=lambda item: item[1], reverse=True )
print(*new_list, sep = "\n", file=f)