[EN] Une faille de sécurité dans nos téléphones

Présentation de "Veritasium"sur une faille qui rend nos téléphones piratables.

Exposing The Flaw In Our Phone System

00:00 I hacked Linus!
00:59 How Steve Jobs and Steve Wozniak hacked the telephone network
02:22 Early history of the telephone
07:08 The kidnapping of Sheikha Latifa
08:41 How Signalling System No. 7 works
11:03 Why SS7 is vulnerable to hacking
12:15 How hackers gain access to phones
16:17 How I was able to spy on Linus’ phone
18:09 How hackers can intercept text messages
21:04 How your location can be tracked via SS7
29:03 How to protect your phone from hacking

La perception du bonheur

"Cyrus North" et  Léo de "DirtyBiology" présentent des vidéos sur le bonheur et sa perception.

Êtes-vous vraiment heureux ?

Je suis parti au Liban à la rencontre de réfugiés syriens. Je leur ai posé à tous la même question : "Selon vous, sur une échelle de 0 à 10, à quel point êtes vous heureux ?".

 Je suis allé dans un pays en guerre pour comprendre la science du bonheur.

 

 

 

 

 

[EN/FR] Dilemme du prisonnier

(FR en 2ème vidéo)

Présentation de "Veritasium"sur le dilemme du prisonnier (théorie des jeux) et quelques réflexions autour de ce sujet.

Quelles sont les meilleurs stratégies? pourquoi? dans quels cas concrets peut utiliser ces résultats? ...

 

What The Prisoner's Dilemma Reveals About Life, The Universe, and Everything

 

 

 

 

Thibaut de la chaine "Monsieur Phi" présente une vidéo sur la théorie des jeux et Kant

Une expérience de philosophie morale

Sommaire

0:00 - Présentation du "jeu" (scénario de base)
3:25 - Dilemme du prisonnier et agent rationnel
9:08 - En quoi le choix rationnel enfreint l'impératif catégorique de Kant
16:39 - Résultats scénario de base
21:35 - Résultats des trois variantes
27:05 - Comparaison H/F, âge, situation sociale, orientation politique
29:54 - Les groupes "pour moi" croient vivre dans un autre monde
32:22 - Synthèse et conclusion

[EN] Sex Work, OnlyFans, Porn, Escorting, Dating, and Human Sexuality

Vidéo interview de "Lex Fridman" avec Aella (Aella is a sex researcher, writer, and sex worker.)
Sex Work, OnlyFans, Porn, Escorting, Dating, and Human Sexuality

OUTLINE:
0:00 - Introduction
1:57 - Uncertainty
9:34 - Sex, power, and death
22:03 - Free will
23:29 - Consciousness
33:24 - Childhood
46:22 - Dancing
59:38 - Casual sex
1:01:53 - Camming
1:16:48 - OnlyFans
1:27:02 - Dating
1:42:54 - Escorting
2:00:55 - Emotion vs reason
2:09:00 - Love
2:16:32 - Polyamory
2:25:26 - Monogamy
2:31:57 - Sex fetishes
2:49:25 - Dominance and submissiveness
2:59:06 - Psychedelics
3:18:18 - Romance
3:25:47 - Body count
3:35:34 - Porn
3:38:53 - Mortality
3:41:29 - AI
3:52:09 - Rationalist discourse
3:56:00 - Meaning of life

Le temps qu'il vous reste (à vivre)

 Léo de "DirtyBiology" présente une méthode permettant de mieux gérer notre temps de vie?

Rapport RTE : Quelle électricité pour demain ?

  Vidéos de "Le Réveilleur" et d'"Osons Causer" sur le rapport de RTE pour l’électricité de demain.

 

Explication et comparaisons des différentes options du rapport de RTE

 

 

Dans cette vidéo, je vous parle de l'évolution du système électrique français en me servant du récent rapport de RTE: Futurs Énergétiques 2050. 
0:00​ • Introduction
2:37 • Consommation énergétique française
5:44 • La transition énergétique en France
7:57​ • Réduire de 40% l'énergie finale ?
11:01 • Future consommation d'électricité
13:38 • Un parc nucléaire vieillissant
15:12 • Tout un système électrique à construire
16:47 • Quelle électricité pour 2050
23:25 • Besoins de flexibilité
26:25 • Foisonnement et interconnexion
28:35 • Pilotage de la demande
30:44 • Moyens de stockage
33:33 • Les centrales au gaz
38:58 • Les coûts économiques
44:55 • Émissions de CO2
48:00 • Crédibilité des scénarios
54:22 • Conclusion
56:03 • Outro

 

 ..

Les MONDES ENGLOUTIS : l'exploration sous-marine

 
Vidéo de "Balade Mentale" sur l'histoire de l'exploration sous-marine.

Né trop tard pour explorer la Terre et trop tôt pour entreprendre un voyage en direction des étoiles, peut être sommes nous vivant juste à temps pour explorer l’océan. Aujourd’hui connu de tous, les premiers humains à avoir été dans l’espace ont inscrit leur noms dans l’histoire, à l’inverse de ceux - à jamais oublié - qui les premiers ont franchis les eaux de surface pour s’immerger. Il n’existe aucune trace gravé, écrite ou dessiné, aucun témoignage des femmes et des hommes qui se sont très certainement, dès l’aube de l’humanité, jetés à l’eau. De ces premières plongées, ne nous sont parvenu que des preuves indirectes, amoncellement de coquillages, de corails ou de perles, qu’il a bien fallu aller chercher, et remonter du fond des eaux. Autant de vestiges qui ont été retrouvés dans des tombes pouvant remonter jusqu’à la très lointaine préhistoire. Les mers, pleines de mystères, de promesses ont rempli nos filets, nous ont nourri, attiré, effrayé, fasciné. Surement bien avant d’être capable de descendre de quelques mètres sous les vagues, nos ancêtres apprirent à dompter les eaux depuis leur surface. Utilisant leur modestes embarcations, pour se déplacer, puis pêcher et commercer, les premiers marins, bravant les dangers et souvent l’inconnu ramenèrent dans leur filets, des contes de leur aventures, et des histoires par milliers. Déformées par le temps, amplifiée elles ont finit par donner une partie de nos myths, peuplant souvent les mers d’êtres surnaturels imaginaires, de dieux, de déesses, et de monstres, dont l'existence permettait d’expliquer et conjurer l'incompris, les drames des départs et des naufrage, de donner un sens aux caprices du destin. Peu à peu, en surface, avec les progrès de la navigation, l’inconnu s’est progressivement vu repoussé par delà l’horizon, tandis que sous l’eau, il est resté là, à proximité du rivage, à quelques mettre sous l’écume des vagues. Pendant des millénaires, nous n’avons su à peu de chose près - rien - de ce qui se cache sous la surface flou, des mondes du dessous. Car pour s’aventurer ici, la seule technologie dont nous disposions s’appelait alors une grande bouffé d’air, et les ombres entraperçues à la surface des eaux, les créatures étranges parfois remontées dans nos filets, ont continué à alimenter pendants des siècles nos mythe et nos légendes ...

La poésie de la science (feat Neil DeGrasse Tyson)

"Stardust - La Chaîne Air & Espace" profite de la sortie de la nouvelle saison de COSMOS pour faire une interview de Neil DeGrasse Tyson, ou ils parlent de la poésie de la science (et en particulier ce qui à un rapport avec l'espace).

Notamment pourquoi continuer a dépenser de l'argent dans le spatial, le rôle géopolitique que ça a, la présence de la science dans notre société, etc.

La méritocratie VS la chance

Monté de la chaine "Linguisticae" se demande si le mérite existe vraiment.

Origine sociale, cercle de connaissance, bon endroit au bon moment, chance, le mérite existe-t-il vraiment?

 

Sur le même sujet, en anglais mais en plus court, il y a également cette vidéo de "Veritasium":

 

[EN] Kathryn Schulz : A propos de nos erreurs

"La plupart d'entre nous sommes prêts à tout pour éviter d'avoir tort. Mais si nous avions tort de faire ça? Kathryn Schulz, "Erreurologue", nous démontre qu'il ne faut pas se contenter d'admettre notre propension à l'erreur, mais que nous devons l'embrasser."

Un renouveau de l'eugénisme : l'eugénisme libéral

Collaboration entre "DirtyBiology" et "Monsieur Phi" sur un renouveau de l'eugénisme.

Avec les avancées technologiques dans le domaine de la biologie (CRISPR-KAS9 par exemple), nous risquons de voir un retour de l'eugénisme... Mais est-ce forcément une mauvaise chose?

 + une vidéo de retours/commentaires supplémentaire:

En supplément, voici une (longue) vidéo de "Mr. Sam" sur un sujet très proche: le transhumanisme

[EN] L'ignorance n'est pas le plus gros problème, savoir des choses qui sont fausses est pire.

Comment peut-on résoudre les problèmes dans le monde si on n'est même pas capable d'avoir une bonne vision de ces problèmes?

Dans ces 2 vidéos (qui ont des parties communes) Hans Rosling et Ola Rosling présentent plein de statistiques méconnues sur le niveau de vie dans le monde, et montrent a quel point on est pire qu'ignorant: on sait des choses fausses

"Ce n’est pas tant ce que les gens ignorent qui cause des problèmes, c’est tout ce qu’ils savent et qui n’est pas vrai."

Fake news ou informations sélectives?

Cette deuxième vidéo est une conférence scénarisée qui reprend et regroupe les conférences habituelles de Hans Rosling (j'aime moins le format scénarisé, mais il a l'avantage de rassembler beaucoup d'informations de ses autres conférences)

Petite remarque, je trouve dommage ces comparaisons avec les singes, car selon le nombre de catégories choisies, on n'obtient pas les mêmes résultats.... Cependant le message global reste juste.

[EN] Relation entre les différences de genre selon le developpement économique et l'égalité des sexes.

Papier qui étudie si les différences de genres dans les préférences (risques, patience, altruisme, et réciprocité négative et positive) augmentent ou diminuent lorsque les personnes vivent dans un pays plus égalitariste. Dit autrement: Le fait de vivre dans un pays économiquement plus développé avec une égalité des sexes entraine-t-il une augmentation ou une diminution des écarts femmes-hommes des préférences.

site d'origine: ici

[EN] Différences de salaire femme/homme aux US: une influence des heures travaillées

Post de blog qui compare les salaires femme/homme aux Etat-Unis selon des critères de temps de travail et de statut familial afin d'identifier ce qui peut causer la différence de salaire selon le genre

Le site web d'origine est ici: https://visme.co/blog/wage-gap/

Ci-dessous une copie (avec une mise en page moins bonne) dans le cas où le site d'origine disparaîtrait

[Edit]: il semblerait que des résultats similaires existent en France selon cette page Wikipédia: ici

 --------

 Sur le même sujet, une vidéo d'"Heu?reka" en VF

 --------- 

(copie du blog / premier lien)

Is the Difference in Work Hours the Real Reason for the Gender Wage Gap? [Interactive Infographic]

Written by:
Federico Anzil

Every year, the Department of Labor issues a report on the pay gap between women and men.
Women earn a median of $30,000¹ per year, while men earn $40,000 per year. In other words, working women earn 75% of what men earn.
But this gap doesn’t take into account the fact that on average, men work more hours than women. According to U.S. census data, men spend an average of 41.0 hours per week at their jobs, while women work an average of 36.3 hours per week.
Many argue that gender discrimination explains a large part of the difference in earnings. Others argue that parenthood and gender roles usually affect women's earnings more than men.
To better understand the pay gap, we classified the respondents according to their marital and parenthood status². The gap is dramatically higher between married couples versus singles without children. For married parents, the gap is even greater.

Created with Visme

But we also found that married fathers work even more than other men, while married mothers work less than married women without kids.
We analyzed the pay gap across hundreds of U.S. occupations. According to our research, in most occupations, the main source of the pay gap lies in the difference between the number of hours spent at work by women and men, and marital status and parenthood explain almost all this difference in working times.
The different behavior of women and men³ has an impact on the gender wage gap. As we will see below, the decision of who does most of the work outside versus who stays at home influences the pay gap in two ways: it modifies the nominal income, but it also influences how much women and men earn per each hour worked⁴.

A few specific examples

Let's take a look at the most common occupation in the US: Managers. This occupation is representative of the overall trend we see in the United States.

Single male managers without kids earn a median of $60,000 per year, while single female managers without kids earn $58,000 per year. On average, single male managers work 43.7 hours per week, while single female managers work 42.3 hours per week.
This means that men earn 3.4% more but work 3.5% more hours per week.
But when we look at the pay gap between married couples, we see a different picture. Both female and male married managers do have a higher salary. But men earn much more than women.

Male married managers without kids earn a median of $90,000, while female married managers without kids earn a median of $62,000. A pay gap of 31%. In other words, women earn $0.69 for each dollar earned by their male counterparts.
A large part of this gap is explained by the number of hours spent at work. Men tend to work more after they marry. The average weekly working hours of males increase 4.3%, while women keep working the same quantity of hours per week. This explains a part of the gap increase.
But the time spent at work does not explain all of the gender pay gap. Married men managers without kids also earn more for each hour at work: they earn $38.40 per hour while married women without kids earn only $28.70. That means that for each hour spent at their jobs, male married managers without kids earn about 34% more than women. As we will see in detail below, the different hourly rate is related to job market trends.
We can see the same pattern across occupations like school teachers, secretaries, nurses, customer service representatives, and a lot of other professions: a small pay gap for singles without kids and a larger pay gap for married people.

Exceptions to the overall trend

We have seen that, for the most common occupations, there is almost no absolute pay gap for singles without kids, and this gap could be explained by the difference in time spent at work. But there are some occupations that do show a gap for this group of people.
Notable examples are drivers, retail salespersons, supervisors and janitors. Interestingly, we can see the same general pattern in these occupations: the uncontrolled gap increases dramatically for married couples, even if they do not have kids.

Created with Visme

The same general pattern repeats itself in occupations where single women without kids earn more than their male counterparts. Some of them are secretaries, customer service representatives, cooks, stock clerks, office clerks and receptionists.

In all of these occupations, the pay gap in favor of women reverts if they marry: married men still earn more than married women.

More time at work also means higher wages

Now, let’s look closely at the different hourly wages paid to women and men. The data shows that there is a persistent difference in the hourly rate earned by women and men, specially for married women and men. But the data also shows that men work more than women.
After taking a closer look at the data, we found a relationship between the hourly wage and the time spent at work. The average hourly pay increases as the number of hours worked per week increases. This is true for both sexes.
In the following chart, we plotted the hourly pay for women and men. To isolate the effect of marriage and parenthood, we took into account only singles without kids.

In the next chart, we can see the average number of hours worked for each group:

For the relevant range of hours worked per week, the average hourly pay increases as the time spent at work increases.
Because men tend to work more hours than women, especially if they are married, and even more if they are married parents, this could explain a large portion of the pay gap.
Also, the previous chart shows that on average, single women without kids are getting paid more than men for every hour spent at work. This could mean that if women worked the same amount of hours as men do, and other conditions remained the same, there would be no pay gap for this group⁵.

What about age and experience?

It is important to note that age and job experience are also relevant factors in the gender gap debate. To isolate the possible effects that age and job experience may have in the pay gap for each of the different groups, we plotted the weighted average of working hours per age for single women and men without kids.

Created with Visme

For singles without kids, there is a very small gap at every age. But for married couples, there is a significant gap in working hours at every age.

If we take into account how the hourly wage varies as men and women get older, the hourly wage of men increases more than the hourly wage of women. The same pattern can be seen in all three groups.

The charts above demonstrate that job experience is correlated with the time spent at work through the years. As years pass, men accumulate more practice and training than women. The job market pays more if the worker has more experience. In other words, the gap widens as men acquire more experience than women⁶.

So what's the real cause of the gender wage gap?

In this article, we found that one of the main sources of the gender pay gap is the fact that, on average, women and men devote a different number of hours to their jobs, specially after marriage and parenthood.
The literature on gender pay gap is very extensive. Different papers focus on diverse causes to explain it. Two of the most mentioned reasons are gender discrimination and motherhood and gender roles.
Gender discrimination against women occurs if a woman is paid less than a man for doing the same job.
If we consider that the quantity of hours devoted to a job determines whether we consider a job to be the same as another, the data doesn’t support the idea of gender discrimination at the aggregate level.
The hourly pay rate for married women is lower than for married men on average, but a probable explanation is because the job market pays less per hour if the number of hours worked decreases, and married women tend to work less. The same pattern can be seen in almost every occupation.
Also, men tend to devote more time to work, thus acquire more experience as years pass by, and the job market pays more if the worker has more experience.
This doesn’t mean that gender discrimination doesn’t exist. Our analysis just shows that, at the aggregate level, most of the gap is not explained by gender discrimination.
Regarding the second aspect of the pay gap, societal ideas of gender roles influence the behavior of women and men. Also, biological factors related to parenthood do play a role in the creation of differences in preferences. Namely, women get pregnant and women breastfeed. These differences between sexes could be a plausible explanation of why women tend to spend more time at home versus their couples, especially after marriage and parenthood⁷.
To conclude and to recap, we can say that, according to our analysis, job market forces and gender preferences in relation to marital status and parenthood could explain almost all of the pay gap. Most of the gap is not the result of gender discrimination.

Methodology

Data Source

We used IPUMS USA to extract the data of the American Community Survey 2017.
Some characteristics of this sample are:
- 1-in-100 national random sample of the population.
- The data include persons in group quarters.
- This is a weighted sample.
- The smallest identifiable geographic unit is the PUMA, containing at least 100,000 persons. PUMAs do not cross state boundaries.
The ACS is the largest household survey that the Census Bureau administers.
The number of cases in this data set is 3,190,040.
We selected the following variables:
- NCHILD Number of own children in the household
- YNGCH Age of youngest own child in household
- SEX Sex
- AGE Age
- MARST Marital status
- OCC Occupation
- UHRSWORK Usual hours worked per week
- INCWAGE Wage and salary income
- PERWT Person weight

Description of the Variables

PERWT
PERWT indicates how many persons in the U.S. population are represented by a given person in an IPUMS sample.
PERWT should be used when conducting a person-level analysis of any IPUMS sample.
NCHILD
NCHILD counts the number of own children (of any age or marital status) residing with each individual. NCHILD includes step-children and adopted children as well as biological children. Persons with no children present are coded "0."
YNGCH
YNGCH reports the age of the youngest own child (if any) residing with each individual, regardless of the child's age or marital status. The highest legitimate age for YNGCH is 98. YNGCH includes step-children and adopted children as well as biological children. Persons with no own children present are coded 99.
SEX
SEX reports whether the person was male or female.
AGE
AGE reports the person's age in years as of the last birthday.
MARST
MARST gives each person's current marital status.
OCC
OCC reports the person's primary occupation, coded into a contemporary census classification scheme. Generally, the primary occupation is the one from which the person earns the most money; if respondents were not sure about this, they were to report the one at which they spent the most time. Unemployed persons were to give their most recent occupation. For persons listing more than one occupation, the samples use the first one listed.
Codenames can be obtained from this url:
https://usa.ipums.org/usa/volii/occ_acs.shtml
UHRSWORK
UHRSWORK reports the number of hours per week that the respondent usually worked, if the person worked during the previous year. The census inquiry relates to the previous calendar year, while the ACS and the PRCS uses the previous 12 months as the reference period.
UHRSWORK is a 2-digit numeric variable that reports the number of hours per week that the respondent usually worked, if the person worked during the previous year. The census inquiry relates to the previous calendar year, while the ACS and the PRCS uses the previous 12 months as the reference period. UHRSWORK specific variable codes for missing, edited, or unidentified observations, observations not applicable (N/A), observations not in universe (NIU), top and bottom value coding, etc. are provided below if applicable by Census year (and data sample if specified).
UHRSWORK Specific Variable Codes
00 = N/A
99 = 99 hours (Top Code)
INCWAGE
INCWAGE reports each respondent's total pre-tax wage and salary income - that is, money received as an employee - for the previous year. The censuses collected information on income received from these sources during the previous calendar year; for the ACS and the PRCS, the reference period was the past 12 months. Sources of income in INCWAGE include wages, salaries, commissions, cash bonuses, tips, and other money income received from an employer. Payments-in-kind or reimbursements for business expenses are not included. See the comparability discussion below for further information.
Amounts are expressed in contemporary dollars, and users studying change over time must adjust for inflation (See INCTOT for Consumer Price Index adjustment factors). The exception is the ACS/PRCS multi-year files, where all dollar amounts have been standardized to dollars as valued in the final year of data included in the file (e.g., 2007 dollars for the 2005-2007 3-year file). Additionally, more detail may be available than exists in the original ACS samples.
User Note: ACS respondents are surveyed throughout the year, and amounts do not reflect calendar year dollars. While the Census Bureau provides an adjustment factor (available in ADJUST), this is an imperfect solution. See the ACS income variables note for further details.
INCWAGE is a 7-digit numeric code reporting each respondent's total pre-tax wage and salary income - that is, money received as an employee - for the previous year. INCWAGE specific variable codes for missing, edited, or unidentified observations, observations not applicable (N/A), observations not in universe (NIU), top and bottom value coding, etc. are provided below by Census year (and data sample if specified).
User Note: Amounts are expressed in contemporary dollars, and users studying change over time must adjust for inflation.
INCWAGE Specific Variable Codes
999999 = N/A
999998 = Missing

Data Preparation

We used Python to import and filter the data. Python is a general-purpose programming language. The libraries Pandas and Numpy libraries were used to import and filter the data.
We removed observations less than 16 years old, unemployed who never worked, not in labor force who last worked more than 5 years ago.
We removed observations that reported N/A or missing wage or salary income.
We removed observations with a code of 0 hours worked
To isolate any possible effect caused by marked differences between women and men when they reach older ages, we took into account only people younger than 70 years old.
To calculate the hourly wage, we used the value 52.143 to calculate how many weeks are in a year. We calculated the number of hours worked per year for each observation, multiplying UHRSWORK ( the number of hours per week that the respondent usually worked, if the person worked during the previous year) by this value.
To arrive at the hourly wage, we divided the wage and salary income for the previous year (INCWAGE) by the number of hours worked per year.
To consider if a person is a parent, we took into account if there is an own child residing with each individual and the age of the youngest child. For this study, we chose the age of 18 as the limit to consider if a person is a parent.
We classified observation according to their marital and parenthood status (according to our definition of parent).

• If an observation is single and no parent, we assigned it to the group “singles no children”
• If an observation is married and no parent, we assigned it to the group “married no children”
• If an observation is married and parent, we assigned it to the group “married with children”

After all this cases had been removed, our sample consisted in 1,509,403 observations.

Descriptive Statistics

To calculate the statistics, we used R and Python. R is a programming language for statistical computing. We used the R library dplyr to create subsets of the sample.
We calculated the weighted statistics for:

• wage and salary income
• hours worked
• hourly wage

For the wage and salary income, we chose the median as the descriptive statistic. Because the mean can be influenced by extreme scores. That means that a small number of high wages could significantly affect the mean, but not the median.
The hourly wage also represent the median.
In the case of the hours worked we chose the mean because there is a natural limit on the worked hours per week, so extreme values cannot be present (the top code for UHRSWORK is 99).
For the occupations, we also merged the dataset with the occupations names. Occupation names can be found at:
https://usa.ipums.org/usa/volii/occ_acs.shtml

Notes

1. 
   
   Median pre-tax wage and salary income. Only working people younger than 70. Not filtering out any outlier, for the rest of the article we filtered out cases that we consider abnormal and not representative. See the methodology section for more information.
2. 
   
   To isolate the effect of parenthood and marital status, we took into account only three groups: Not married without children, married without children and married with children. Further research could also take into account more categories, like single parents.
3. 
   
   Different choices can be agreed upon and voluntary decisions of both members of the couple, although influenced by sociological aspects and gender roles.
4. 
   
   To calculate the hourly pay, we used the average number of hours per week that the respondent usually worked.
5. 
   
   Or a small gap in favor of women.
6. 
   
   Also, not plotted here, the difference in worked hours slightly increases as men and women get older.
7. 
   
   These can be agreed and voluntary decisions of both member of the couple, although, influenced by sociological aspects and gender roles.
8. 
   
   Steven Ruggles, Sarah Flood, Ronald Goeken, Josiah Grover, Erin Meyer, Jose Pacas, and Matthew Sobek. IPUMS USA: Version 8.0 [dataset]. Minneapolis, MN: IPUMS, 2018. https://doi.org/10.18128/D010.V8.0

Des ONG dogmatiques

Voici quelques exemples d'ONG dogmatique: elle ignore ou modifie ce que dit la science pour ne pas changer leurs opinions politiques.

Rodolphe de la chaine "Le Réveilleur" sur Greenpeace:

(le tableau est accessible dans le rapport du GIEC à la page 1335 disponible ici)

Rodolphe de la chaine "Le Réveilleur" sur la fermeture de Fessenheim:

Dans cette vidéo, je vous parle de la fermeture de la centrale nucléaire de Fessenheim.
0:00 • Introduction
1:04 • Production électrique
1:51 • Ancienneté de la centrale
4:44 • Risque sismique
8:55 • L'analyse du cycle de vie permet de quantifier les émissions de CO2
11:27 • Émissions de CO2 par kWh de différents moyens de production électriques
12:21 • Estimation du surplus d'émissions de CO2 liée à la fermeture de Fessenheim
17:21 • Aspects économiques
20:04 • Quelques avantages
21:47 • Effet falaise et remplacement du parc nucléaire existant
23:18 • Aspects politiques et désinformation sur le nucléaire 

 

La chaine "DeBunKer des Etoiles" sur les médias qui reprennent sans nuances ni vérifications les chiffres d'OXFAM

[EN] Ecart entre population et scientifique sur des sujets importants

Voici un site internet qui publie des données montrant l'écart de vue entre les citoyens et les scientifiques américains sur de nombreux sujets (entre autres: OGM, recherche animale, pesticide, réchauffement climatique, nucléaire, vaccins, ... )

https://www.pewinternet.org/interactives/public-scientists-opinion-gap/

[EN] Comment un tweet peut ruiner votre vie

Jon Ronson présente un exemple où Twitter s'est transformé en tribunal populaire

[EN] Qu'est-ce que le Hasard?

Michael de "Vsauce" et Dereck de "Veritasium" se posent des questions sur ce qu'est le hasard.

7 arguments CONTRE la démocratie

Lê de "Science4All" présente quelques arguments contre la démocratie.

Savoir se remettre en question et percevoir l'étendue de notre ignorance

Lê de "Science4All" présente sa remise en question suite à la découverte de l'étendue de son ignorance.

Comment se remettre en question