Facebook has been accused of helping to ‘brainwash’ children as young as five with maths lessons that teach pupils to use the ‘correct’ pronouns for ‘non-binary’ people.
The social media giant is the main sponsor of School Diversity Week, a campaign endorsed by Gavin Williamson‘s Department for Education and adopted by hundreds of head teachers.
Lesson packs for Key Stage 1 pupils aged five to seven tell children that people have a ‘gender identity’ that may not match their sex at birth, and that some people feel like they are neither male nor female.
Facebook has been accused of helping to ‘brainwash’ children as young as five with maths lessons that teach pupils to use the ‘correct’ pronouns for ‘non-binary’ people. Above, a maths question included in the Just Like Us lessons pack
The packs include lessons which appear to give more importance to the use of pronouns than sums.
One question says: ‘Harley, (a non-binary and gender-non-conforming person using they/them pronouns) has volunteered to give bottles of water to runners of a Marathon [sic]. They have 15 bottles of water but there are a total of 25 runners in the marathon. How many more bottles of water does Harley need to buy?’
In accompanying notes, teachers are told: ‘Explain that Harley does not like to be referred to as he/she or him/her so when we describe Harley we use they/them.’
The lesson plan adds: ‘If any children ask why, then you could explain ‘Harley doesn’t feel like a boy or a girl, so by using they/them pronouns they don’t have to feel they are either’. Ask the children to discuss with their partner.’
The social media giant is the main sponsor of School Diversity Week, a campaign endorsed by Gavin Williamson’s Department for Education and adopted by hundreds of head teachers
The campaign pack also tells teachers to give Key Stage 1 children the following definition of ‘transgender’: ‘When a person is born they are assigned a gender. For a transgender person the gender they have been assigned does not match how they feel inside.
‘So someone who is told they are a boy feels like they are a girl, or someone who is told they are a girl, feels like they are a boy.’
Another maths problem reads: ‘Jas (a gender non-conforming person using she/her pronouns) is making an apple pie for their friends and family…’ before going on to pose a question about the number of apples required to make the pie.
The teaching materials were produced by Just Like Us, a charity whose director of education is Emma Fay, a self-described ‘queer’ charity worker.
Despite growing concern in Conservative circles about trans activists’ influence in the public sector, the Department for Education has publicly backed the Just Like Us campaign, tweeting an endorsement and encouraging schools to take part.
The materials were revealed by the Safe Schools Alliance, a group of parents and teachers worried about trans activism in education. The group has written to Mr Williamson (above) about their concerns
‘We’re proud to support Just Like Us #SchoolDiversityWeek,’ the department said on Twitter.
The campaign was also endorsed by senior Labour MPs including Lucy Powell, the shadow housing minister, and Diane Abbott.
Ed Davey, the leader of the Liberal Democrats, also tweeted his support.
In a video message, Nick Baughan, a Facebook executive, said that the firm was proud to support the campaign.
The social media company has promoted multiple gender identities to its users worldwide, offering as many as 71 different genders for user profiles.
The materials were revealed by the Safe Schools Alliance, a group of parents and teachers worried about trans activism in education. The group has written to Mr Williamson about their concerns.
Tanya Carter, a spokeswoman for the alliance, described the materials as ‘political indoctrination’.
She said: ‘There is nothing ‘diverse’ about Just Like Us UK’s resources for School Diversity Week. All the materials are riddled with an obsession with pronouns.
‘This is not inclusion. This is indoctrination into a contentious political viewpoint.’
Baroness Foster, a Conservative peer, described the materials as ‘brainwashing to the nth degree,’ adding: ‘This is just appalling.’
A spokesman for Facebook said: ‘Our employee Pride group in the UK is pleased to be supporting School Diversity Week again this year – an important initiative which thousands of schools across the country are taking part in.’
Updated July 18: Developers and advertising partners may be required to share information on their app’s privacy practices in third party app stores, such as Google Play and the Apple App Store, including the functionality of SDKs provided by Meta. To help make it easier for you to complete these requirements, we have consolidated information that explains our data collection practices for the Facebook and Audience Network SDKs.
To provide functionality within the Facebook SDK, we may receive and process certain contact, location, identifier, and device information associated with Facebook users and their use of your application. The information we receive depends on what SDK features 3rd party applications use and we have structured the document below according to these features.
App Ads, Facebook Analytics, & App Events
Facebook App Events allow you to measure the performance of your app using Facebook Analytics, measure conversions associated with Facebook ads, and build audiences to acquire new users as well as re-engage existing users. There are a number of different ways your app can use app events to keep track of when people take specific actions such as installing your app or completing a purchase.
With Facebook SDK, there are app events that are automatically logged (app installs, app launches, and in-app purchases) and collected for Facebook Analytics unless you disable automatic event logging. Developers determine what events to send to Facebook from a list of standard events, or via a custom event.
Developers may also send us additional user contact information in code, or via the Events Manager. Advanced matching functionality may use the following data, if sent:
email address, name, phone number, physical address (city, state or province, zip or postal code and country), gender, and date of birth.
There are two scenarios for applications that use Facebook Login via the Facebook SDK: Authenticated Sign Up or Sign In, and User Data Access via Permissions. For authentication, a unique, app-specific identifier tied to a user’s Facebook Account enables the user to sign in to your app. For Data Access, a user must explicitly grant your app permission to access data.
Note: Since Facebook Login is part of the Facebook SDK, we may collect other information referenced here when you use Facebook Login, depending on your settings.
We may also receive and process the following information if your app is integrated with the Facebook SDK:
Device attributes, such as device model and screen dimensions, CPU core, storage size, SDK version, OS and app versions, and app package name; and
Networking information, such as the name of the mobile operator or ISP, language, time zone, and IP address.
Audience Network SDK
We may receive and process the following information when you use the Audience Network SDK to integrate Audience Network ads in your app:
Device attributes, such as device model and screen dimensions, operating system, mediation platform and SDK versions; and
Ad performance information, such as impressions, clicks, placement, and viewability.
This article was written by Omer Dunay, Kun Jiang, Nachi Nagappan, Matt Bridges and Karim Nakad.
At Meta, Python is one of the most used programming languages in terms of both lines of code and number of users. Everyday, we have thousands of developers working with Python to launch new features, fix bugs and develop the most sophisticated machine learning models. As such, it is important to ensure that our Python developers are productive and efficient by giving them state-of-the-art tools.
Today we introduce Wasabi, a Python language service that implements the language server protocol (LSP) and is designed to help our developers use Python easier and faster. Wasabi assists our developers to write Python code with a series of advanced features, including:
Lints and diagnostics: These are available as the user types.
Auto import quick fix: This is available for undefined-variable lint.
Global symbols autocomplete: When a user types a prefix, all symbols (e.g. function names, class names) that are defined in other files and start with that prefix will appear in the autocomplete suggestion automatically.
Organize Imports + Remove unused: A quick fix that removes all unused imports and reformats the import section according to pep8 rules. This feature is powered by other tools that are built inside Meta such as libCST that helps with safe code refactoring.
Python snippets: Snippet suggestions are available as the user types for common code patterns.
Additionally, Wasabi is a surface-agnostic service that can be deployed into multiple code repositories and various development environments (e.g., VSCode, Bento Notebook). Since its debut, Wasabi has been adopted by tens of thousands of Python users at Meta across Facebook, Instagram, Infrastructure teams and many more.
Figure 1: Example for global symbols autocomplete, one of Wasabi’s features
Language Services at Meta Scale
A major design requirement for language services is low latency / user responsiveness. Autocomplete suggestions, lints and quickFixes should appear to the developer immediately as they type.
At Meta, code is organized in a monorepo, meaning that developers have access to all python files as they develop. This approach has major advantages for the developer workflow including better discoverability, transparency, easier to share libraries and increased collaboration between teams. It also introduces unique challenges for building developer tools such as language services that need to handle hundreds of thousands of files.
The scaling problem is one of the reasons that we tried to avoid using off-the-shelf language services available in the industry (e.g., pyright, jedi) to perform those operations. Most of those tools were built in the mindset of a relatively small to medium workspace of projects, maybe with the assumptions of thousands of files for large projects for operations that require o(repo) information.
For example, consider the “auto import” quick fix for undefined variables. In order to suggest all available symbols the language server needs to read all source files, the quick fix parses them and keeps an in-memory cache of all parsed symbols in order to respond to requests.
While this may scale to be performed in a single process on the development machine for small-medium repositories, this approach doesn’t scale in the monorepo use case. Reading and parsing hundreds of thousands of files can take many minutes, which means slow startup times and frustrated developers. Moving to an in-memory cache might help latency, but also may not fit in a single machine’s memory.
For example, assume an average python file takes roughly 10ms to be parsed and to extract symbols in a standard error recoverable parser. This means that on 1000 files it can take 10 seconds to initialize which is a fairly reasonable startup time. Running it on 1M files would take 166 minutes which is obviously a too lengthy startup time.
How Wasabi Works
Offline + Online Processing:
In order to support low latency in Meta scale repositories, Wasabi is powered by two phases of parsing, background processing (offline) done by an external indexers, and local processing of locally changed “dirty files” (online):
A background process indexes all committed source files and maintains the parsed symbols in a special database (glean) that is designed for storing code symbol information.
Wasabi, which is a local process running on the user machine, calculates the delta between the base revision, stack of diffs and uncommitted changes that the user currently has, and extracts symbols only out of those “dirty” files. Since this set of “dirty” files is relatively small, the operation is performed very fast.
Upon an LSP request such as auto import, Wasabi parses the abstract syntax tree (AST) of the file, then based on the context of the cursor, creates a query for both glean and local changes symbols, merges the results and returns it to the user.
As a result, all Wasabi features are low latency and available to the user seamlessly as they type.
Note: Wasabi currently doesn’t handle the potential delta between the revision that glean indexed (happens once every few hours) and the locally base revision that the user currently has. We plan on adding that in the future.
Figure 2: Wasabi’s high level architecture
Ranking the Results
In some cases, due to the scale of the repository, there may be many valid suggestions in the set of results. For example, consider “auto import” suggestions for the “utils” symbol. There may be many modules that define a class named “utils” across the repository, therefore we invest in ranking the results to ensure that users see the most relevant suggestions on the top.
For example, auto import ranking is done by taking into account:
The distance of the suggested module directory path from the directory paths of modules that are already imported in this file.
The distance of the suggested module directory path from the current directory path of the local file.
Whether the file has been locally changed (“dirty” files are ranked higher).
Usage: The number of occurrences the import statement was used by other files in the repository.
To measure our success, we measured the index in the suggestion list of an accepted suggestion and noted that in almost all cases the accepted suggestion was ranked in one of top 3 suggestions.
Positive feedbacks from developers
After launching Wasabi to several pilot runs inside Meta, we have received numerous positive feedbacks from our developers. Here is one example of the quote from a software engineer at Instagram:
“I’ve been using Wasabi for a couple months now, it’s been a boon to my productivity! Working in Instagram Server, especially on larger files, warnings from pyre are fairly slow. With Wasabi, they’re lightning fast 😃!”
“I use features like spelling errors and auto import several times an hour. This probably makes my development workflow 10% faster on average (rough guess, might be more, definitely not less), a pretty huge improvement!”
As noted above, Wasabi has made a meaningful change to keep our developers productive and make them feel delightful.
The metric to measure authoring velocity
In order to quantitatively understand how much value Wasabi has delivered to our Python developers, we have considered a number of metrics to measure its impact. Ultimately, we landed on a metric that we call ‘Authoring Velocity’ to measure how fast developers write code. In essence, Authoring Velocity is the inverse function of the time taken on a specific diff (a collection of code changes) during the authoring stage. The authoring stage starts from the timestamp when a developer checks out from the source control repo to the timestamp when the diff is created. We have also normalized it against the number of lines of code changed in the diff, as a proxy for diff size, to offset any possible variance. The greater the value for ‘Authoring Velocity,’ the faster we think developers write their code.
With the metric defined, we ran an experiment to measure the difference that Wasabi brings to our developers. Specifically, we selected ~700 developers who had never used Wasabi before, and then randomly put them into two independent groups at a 50:50 split ratio. For these developers in the test group, they were enabled with Wasabi when they wrote in Python, whereas there was no change for those in the control group. For both groups, we compare the changes in relative metric values before and after the Wasabi enablement. From our results, we find that for developers in the test group, the median value of authoring velocity has increased by 20% after they started using Wasabi. Meanwhile, we don’t see any significant change in the control group before and after, which is expected.
Figure 4: Authoring Velocity measurements for control and test groups, before and after Wasabi was rolled out to the test group.
With Python’s unprecedented growth, it is an exciting time to be working in the area to make it better and handy to use. Together with its advanced features, Wasabi has successfully improved developers’ productivity at Meta, allowing them to write Python faster and easier with a positive developer experience. We hope that our prototype and findings can benefit more people in the broader Python community.