• La messagerie interne d’Amazon interdira les mots « syndicat » et « injuste »
    https://www.nextinpact.com/lebrief/68852/la-messagerie-interne-damazon-interdira-mots-syndicat-et-injuste

    La nouvelle application de messagerie interne d’Amazon interdira des mots comme « syndicat », « augmentation de salaire », « travail d’esclave » ou « c’est stupide », révèlent des documents examinés par The Intercept.

    L’objectif principal du programme serait de « favoriser le bonheur des travailleurs, ainsi que leur productivité ». De façon somme toute « orwellienne », Amazon y inclut un « contrôleur automatique des mauvais mots » constitué d’une liste noire destinée à signaler et empêcher automatiquement les employés d’envoyer un message contenant certains mots clés inappropriés.

    En plus des grossièretés, on y trouve de nombreux termes pertinents, comme « éthique », « injuste », « liberté », « indemnisation », « harcèlement » ou encore « ceci est préoccupant ».

    Un porte-parole d’Amazon précise que l’application est au stade de l’expérimentation, et que « beaucoup » de ces mots-clefs seront à terme éliminés : « les seuls types de mots qui peuvent être filtrés sont ceux qui sont offensants ou harcelants, afin de protéger nos équipes. »

    Ces révélations surviennent alors qu’un centre de distribution à New York est devenu le premier site d’Amazon à se doter d’un syndicat. « Doté d’un budget de 120 000 dollars seulement, l’Amazon Labour Union a en effet réussi à vaincre le mastodonte de 1 500 milliards de dollars, qui avait par ailleurs dépensé 4,3 millions de dollars en consultants antisyndicaux rien qu’en 2021 », ajoutent nos confrères.

    #Amazon #Orwell #Surveillance #Psychologie_comportementale #Syndicat

  • New Amazon Worker Chat App Would Ban Words Like “Union”
    https://theintercept.com/2022/04/04/amazon-union-living-wage-restrooms-chat-app

    Orwellien !!!

    Also: “Grievance,” “slave labor,” “This is dumb,” “living wage,” “diversity,” “vaccine,” and others.
    Ken Klippenstein
    Ken Klippenstein

    April 4 2022, 9:27 p.m.

    Amazon will block and flag employee posts on a planned internal messaging app that contain keywords pertaining to labor unions, according to internal company documents reviewed by The Intercept. An automatic word monitor would also block a variety of terms that could represent potential critiques of Amazon’s working conditions, like “slave labor,” “prison,” and “plantation,” as well as “restrooms” — presumably related to reports of Amazon employees relieving themselves in bottles to meet punishing quotas.

    “Our teams are always thinking about new ways to help employees engage with each other,” said Amazon spokesperson Barbara M. Agrait. “This particular program has not been approved yet and may change significantly or even never launch at all.”

    In November 2021, Amazon convened a high-level meeting in which top executives discussed plans to create an internal social media program that would let employees recognize co-workers’ performance with posts called “Shout-Outs,” according to a source with direct knowledge.
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    The major goal of the program, Amazon’s head of worldwide consumer business, Dave Clark, said, was to reduce employee attrition by fostering happiness among workers — and also productivity. Shout-Outs would be part of a gamified rewards system in which employees are awarded virtual stars and badges for activities that “add direct business value,” documents state. At the meeting, Clark remarked that “some people are insane star collectors.”

    But company officials also warned of what they called “the dark side of social media” and decided to actively monitor posts in order to ensure a “positive community.” At the meeting, Clark suggested that the program should resemble an online dating app like Bumble, which allows individuals to engage one on one, rather than a more forum-like platform like Facebook.
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    Following the meeting, an “auto bad word monitor” was devised, constituting a blacklist that would flag and automatically block employees from sending a message that contains any profane or inappropriate keywords. In addition to profanities, however, the terms include many relevant to organized labor, including “union,” “grievance,” “pay raise,” and “compensation.” Other banned keywords include terms like “ethics,” “unfair,” “slave,” “master,” “freedom,” “diversity,” “injustice,” and “fairness.” Even some phrases like “This is concerning” will be banned.
    Do you work for Amazon? Text tips to Ken Klippenstein via Signal at 202-510-1268.

    “With free text, we risk people writing Shout-Outs that generate negative sentiments among the viewers and the receivers,” a document summarizing the program states. “We want to lean towards being restrictive on the content that can be posted to prevent a negative associate experience.”

    In addition to the automated system, managers will have the authority to flag or suppress any Shout-Outs that they find inappropriate, the documents show.

    A pilot program is slated to launch later this month. In addition to slurs and swear words, the planned list includes the following words:

    I hate
    Union
    Fire
    Terminated
    Compensation
    Pay Raise
    Bullying
    Harassment
    I don’t care
    Rude
    This is concerning
    Stupid
    This is dumb
    Prison
    Threat
    Petition
    Grievance
    Injustice
    Diversity
    Ethics
    Fairness

    Accessibility
    Vaccine
    Senior Ops
    Living Wage
    Representation
    Unfair
    Favoritism
    Rate
    TOT
    Unite/unity
    Plantation
    Slave
    Slave labor
    Master
    Concerned
    Freedom
    Restrooms
    Robots
    Trash
    Committee
    Coalition

    “If it does launch at some point down the road,” said the Amazon spokesperson, “there are no plans for many of the words you’re calling out to be screened. The only kinds of words that may be screened are ones that are offensive or harassing, which is intended to protect our team.”

    Amazon has experimented with social media programs in the past. In 2018, the company launched a pilot program in which employees were handpicked to form a Twitter army advocating for the company, as The Intercept reported. The workers were selected for their “great sense of humor,” leaked documents showed.

    On Friday, Amazon workers at a fulfillment center in Staten Island, New York, stunned the nation by becoming the first Amazon location to successfully unionize. This came as a shock to many because it was achieved by an independent union not affiliated with an established union and that operated on a shoestring budget. With a budget of $120,000, the Amazon Labor Union managed to defeat the $1.5 trillion behemoth, which spent $4.3 million on anti-union consultants in 2021 alone.
    Related
    Amazon Did Everything It Could to Bust the Staten Island Union. They Overcame It All.

    Adding to the David-and-Goliath overtones, the Amazon Labor Union’s president, Christian Smalls, a 33-year old former rapper, had been fired by the company after leading a small walkout calling for better workplace protections against the coronavirus in 2020. Amazon executives denigrated Smalls, who is Black, as “not smart or articulate” during a meeting with then-CEO Jeff Bezos, according to leaked memo reported by Vice News.

    Safety issues have been a perennial concern for Amazon workers. In December, a tornado killed six Amazon employees in a warehouse in Edwardsville, Illinois. Many employees said that they had received virtually no emergency training, as The Intercept reported. (The House Oversight Committee recently launched an investigation into Amazon’s workplace safety policies.)

    In 2020, workers at an Amazon fulfillment center in Bessemer, Alabama, tried to join the Retail, Wholesale and Department Store Union. The attempt became unusually high-profile, attracting the attention of President Joe Biden, who released a statement saying, “Every worker should have a free and fair choice to join a union … without intimidation or threats by employers.”

    The Bessemer vote failed, but the National Labor Relations Board ordered a new election, citing undue interference by Amazon. The Bessemer warehouse held a second vote that was also counted last week, and while the initial tally favored Amazon, the vote was much closer than the previous one and will ultimately depend on the results of challenged ballots.

    Amazon released a statement Friday saying that it is considering filing an objection to the Staten Island union vote, alleging interference by the NLRB.

    Update: April 4, 2022, 5:17 p.m. ET
    The headline and article have been updated to emphasize that the app is still in the planning phase and has not yet launched. It has also been updated to include comment from Amazon denying that “many” of the words obtained by The Intercept would be screened out.

    #Amazon #Orwell #Surveillance #Psychologie_comportementale

  • Comment gérer son stress et celui de ses élèves lors du déconfinement ? (INOVAND, Hôpital Robert Debré)
    https://www.pedopsydebre.org/post/comment-gérer-son-stress-et-celui-de-ses-élèves-lors-du-déconfinement

    Le déconfinement nous inquiète tous, en tant qu’adultes mais aussi en tant que professionnels de l’enfance. La reprise du travail en présentiel est envisagée par les enseignants comme particulièrement difficile : non seulement les modalités de reprise restent incertaines, mais en plus la crainte d’être infecté par les élèves est très importante.

    #école #stress #confinement

    • # J’applique 3 règles de prévention : je hiérarchise, je m’épargne et je me montre indulgent envers moi-même.

      Je hiérarchise : Posez-vous la question suivante : « Qu’est-ce qui est le plus important tout de suite ? » Exemple : Faire à manger ou répondre à mon enfant qui a besoin de moi à cet instant ?

      Je hiérarchise : Posez-vous la question suivante : « Qu’est-ce qui est le plus important tout de suite ? » Exemple : que vite les enfants de 2 à 10 ans retournent à l’école ou la crèche, dans un environnement angoissé angoissant et pour s’y voir interdits de bouger, se toucher, échanger... ou bien imaginer d’autres types de déconfinement pour et avec elles/eux ?

    • Comment gérer le stress de mes élèves ?
      La gestion du stress de la classe n’est jamais simple. Il y a une vraie dynamique de classe qu’il est important de mobiliser. Vous connaissez parfaitement votre classe, et vous verrez combien finalement les enfants sont heureux de revenir à l’école et de reprendre leurs habitudes.
      Cependant, cela ne saurait cacher un certain nombre d’inquiétudes chez vos élèves.

      * Elles sont toutes assez communes, et donc vous avez déjà l’habitude de les gérer. Elles seront juste un peu plus intenses ou présentes chez un plus grand nombre d’élèves. Il peut s’agir par exemple de la peur de résultats scolaires insuffisants, de l’échec, de la séparation avec leur famille, d’un déménagement qui aura lieu prochainement...

      Comment rassurer les enseignant·es et les inciter à retourner bosser ? Avec quelques conseils pour « se sentir bien »
      #psychologie_comportementale

  • High score, low pay : why the gig economy loves gamification | Business | The Guardian
    https://www.theguardian.com/business/2018/nov/20/high-score-low-pay-gamification-lyft-uber-drivers-ride-hailing-gig-econ

    Using ratings, competitions and bonuses to incentivise workers isn’t new – but as I found when I became a Lyft driver, the gig economy is taking it to another level.

    Every week, it sends its drivers a personalised “Weekly Feedback Summary”. This includes passenger comments from the previous week’s rides and a freshly calculated driver rating. It also contains a bar graph showing how a driver’s current rating “stacks up” against previous weeks, and tells them whether they have been “flagged” for cleanliness, friendliness, navigation or safety.

    At first, I looked forward to my summaries; for the most part, they were a welcome boost to my self-esteem. My rating consistently fluctuated between 4.89 stars and 4.96 stars, and the comments said things like: “Good driver, positive attitude” and “Thanks for getting me to the airport on time!!” There was the occasional critique, such as “She weird”, or just “Attitude”, but overall, the comments served as a kind of positive reinforcement mechanism. I felt good knowing that I was helping people and that people liked me.

    But one week, after completing what felt like a million rides, I opened my feedback summary to discover that my rating had plummeted from a 4.91 (“Awesome”) to a 4.79 (“OK”), without comment. Stunned, I combed through my ride history trying to recall any unusual interactions or disgruntled passengers. Nothing. What happened? What did I do? I felt sick to my stomach.

    Because driver ratings are calculated using your last 100 passenger reviews, one logical solution is to crowd out the old, bad ratings with new, presumably better ratings as fast as humanly possible. And that is exactly what I did.

    In a certain sense, Kalanick is right. Unlike employees in a spatially fixed worksite (the factory, the office, the distribution centre), rideshare drivers are technically free to choose when they work, where they work and for how long. They are liberated from the constraining rhythms of conventional employment or shift work. But that apparent freedom poses a unique challenge to the platforms’ need to provide reliable, “on demand” service to their riders – and so a driver’s freedom has to be aggressively, if subtly, managed. One of the main ways these companies have sought to do this is through the use of gamification.

    Simply defined, gamification is the use of game elements – point-scoring, levels, competition with others, measurable evidence of accomplishment, ratings and rules of play – in non-game contexts. Games deliver an instantaneous, visceral experience of success and reward, and they are increasingly used in the workplace to promote emotional engagement with the work process, to increase workers’ psychological investment in completing otherwise uninspiring tasks, and to influence, or “nudge”, workers’ behaviour. This is what my weekly feedback summary, my starred ratings and other gamified features of the Lyft app did.

    There is a growing body of evidence to suggest that gamifying business operations has real, quantifiable effects. Target, the US-based retail giant, reports that gamifying its in-store checkout process has resulted in lower customer wait times and shorter lines. During checkout, a cashier’s screen flashes green if items are scanned at an “optimum rate”. If the cashier goes too slowly, the screen flashes red. Scores are logged and cashiers are expected to maintain an 88% green rating. In online communities for Target employees, cashiers compare scores, share techniques, and bemoan the game’s most challenging obstacles.
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    But colour-coding checkout screens is a pretty rudimental kind of gamification. In the world of ride-hailing work, where almost the entirety of one’s activity is prompted and guided by screen – and where everything can be measured, logged and analysed – there are few limitations on what can be gamified.

    Every Sunday morning, I receive an algorithmically generated “challenge” from Lyft that goes something like this: “Complete 34 rides between the hours of 5am on Monday and 5am on Sunday to receive a $63 bonus.” I scroll down, concerned about the declining value of my bonuses, which once hovered around $100-$220 per week, but have now dropped to less than half that.

    “Click here to accept this challenge.” I tap the screen to accept. Now, whenever I log into driver mode, a stat meter will appear showing my progress: only 21 more rides before I hit my first bonus.

    In addition to enticing drivers to show up when and where demand hits, one of the main goals of this gamification is worker retention. According to Uber, 50% of drivers stop using the application within their first two months, and a recent report from the Institute of Transportation Studies at the University of California in Davis suggests that just 4% of ride-hail drivers make it past their first year.

    Before Lyft rolled out weekly ride challenges, there was the “Power Driver Bonus”, a weekly challenge that required drivers to complete a set number of regular rides. I sometimes worked more than 50 hours per week trying to secure my PDB, which often meant driving in unsafe conditions, at irregular hours and accepting nearly every ride request, including those that felt potentially dangerous (I am thinking specifically of an extremely drunk and visibly agitated late-night passenger).

    Of course, this was largely motivated by a real need for a boost in my weekly earnings. But, in addition to a hope that I would somehow transcend Lyft’s crappy economics, the intensity with which I pursued my PDBs was also the result of what Burawoy observed four decades ago: a bizarre desire to beat the game.

    Former Google “design ethicist” Tristan Harris has also described how the “pull-to-refresh” mechanism used in most social media feeds mimics the clever architecture of a slot machine: users never know when they are going to experience gratification – a dozen new likes or retweets – but they know that gratification will eventually come. This unpredictability is addictive: behavioural psychologists have long understood that gambling uses variable reinforcement schedules – unpredictable intervals of uncertainty, anticipation and feedback – to condition players into playing just one more round.

    It is not uncommon to hear ride-hailing drivers compare even the mundane act of operating their vehicles to the immersive and addictive experience of playing a video game or a slot machine. In an article published by the Financial Times, long-time driver Herb Croakley put it perfectly: “It gets to a point where the app sort of takes over your motor functions in a way. It becomes almost like a hypnotic experience. You can talk to drivers and you’ll hear them say things like, I just drove a bunch of Uber pools for two hours, I probably picked up 30–40 people and I have no idea where I went. In that state, they are literally just listening to the sounds [of the driver’s apps]. Stopping when they said stop, pick up when they say pick up, turn when they say turn. You get into a rhythm of that, and you begin to feel almost like an android.”

    In their foundational text Algorithmic Labor and Information Asymmetries: A Case Study of Uber’s Drivers, Alex Rosenblat and Luke Stark write: “Uber’s self-proclaimed role as a connective intermediary belies the important employment structures and hierarchies that emerge through its software and interface design.” “Algorithmic management” is the term Rosenblat and Stark use to describe the mechanisms through which Uber and Lyft drivers are directed. To be clear, there is no singular algorithm. Rather, there are a number of algorithms operating and interacting with one another at any given moment. Taken together, they produce a seamless system of automatic decision-making that requires very little human intervention.

    For many on-demand platforms, algorithmic management has completely replaced the decision-making roles previously occupied by shift supervisors, foremen and middle- to upper- level management. Uber actually refers to its algorithms as “decision engines”. These “decision engines” track, log and crunch millions of metrics every day, from ride frequency to the harshness with which individual drivers brake. It then uses these analytics to deliver gamified prompts perfectly matched to drivers’ data profiles.

    To increase the prospect of surge pricing, drivers in online forums regularly propose deliberate, coordinated, mass “log-offs” with the expectation that a sudden drop in available drivers will “trick” the algorithm into generating higher surges. I have never seen one work, but the authors of a recently published paper say that mass log-offs are occasionally successful.

    Viewed from another angle, though, mass log-offs can be understood as good, old-fashioned work stoppages. The temporary and purposeful cessation of work as a form of protest is the core of strike action, and remains the sharpest weapon workers have to fight exploitation. But the ability to log-off en masse has not assumed a particularly emancipatory function.

    After weeks of driving like a maniac in order to restore my higher-than-average driver rating, I managed to raise it back up to a 4.93. Although it felt great, it is almost shameful and astonishing to admit that one’s rating, so long as it stays above 4.6, has no actual bearing on anything other than your sense of self-worth. You do not receive a weekly bonus for being a highly rated driver. Your rate of pay does not increase for being a highly rated driver. In fact, I was losing money trying to flatter customers with candy and keep my car scrupulously clean. And yet, I wanted to be a highly rated driver.
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    And this is the thing that is so brilliant and awful about the gamification of Lyft and Uber: it preys on our desire to be of service, to be liked, to be good. On weeks that I am rated highly, I am more motivated to drive. On weeks that I am rated poorly, I am more motivated to drive. It works on me, even though I know better. To date, I have completed more than 2,200 rides.

    #Lyft #Uber #Travail #Psychologie_comportementale #Gamification #Néo_management #Lutte_des_classes