fidzu : debian

This needs to be clear: systemd is under attack by a trolling campaign orchestrated by fascist elements. Nobody is forced to like or use systemd, but anybody who wants to pick a side should know the facts.

Recently, the free software Nazi bar crowd styling themselves as "concerned citizens" has tried to start a moral panic by saying that systemd is implementing age verification checks or that somehow it will require providing personally identifiable information.

This is a lie: the facts are simply that the systemd users database has gained an optional "date of birth" field, which the desktop environments may use or not as they deem appropriate. Of course there is no "identity verification" or requirements to provide any data, which in any case would not be shared beyond authorized local applications.

While the multiple recent bills proposing that general purpose operating systems implement age verification mechanisms are often concerning, both from a social and technical point of view, this is not the topic being discussed here. They are often suboptimal, but for a long time I have been opposing attempts to implement parental control at the network level and argued that it should be managed locally, by parents on their own machines: I cannot see why I should outright reject an attempt to implement the infrastructure to do that.

If we want to keep age-appropriate controls out of the hands of centralized authorities, the alternative is giving families the means to manage it themselves: this is what this field enables. Whether desktop environments use it for parental controls, for birthday reminders, or for nothing at all, is their users' decision.

By the way, the original UNIX users database has allowed storing PII in the GECOS field since it was invented in the '70s. Similar fields are also specified by many popular LDAP schemes: adding such an optional field is consistent with the UNIX tradition.

And while we are at it, let's also refute the other smear campaign started by the same people: the systemd project is not accepting "AI slop". What happened is that a documentation file for the benefit of coding agents was added to the repository. To be clear: agents still cannot submit merge requests. The file itself remarks that all contributions must be reviewed in detail by humans, and this is basically the same policy used by the Linux kernel.

23 Mar 2026 3:47pm GMT

Note: I have not published blog posts about my academic papers over the past few years. To ensure that my blog contains a more comprehensive record of my published papers and to surface them for folks who missed them, I will periodically (re) publish blog posts about some "older" published projects. This post draws material from a previously published post by Kaylea Champion on the Community Data Science Blog.

Taboo subjects-such as sexuality and mental health-are as important to discuss as they are difficult to raise in conversation. Although many people turn to online resources for information on taboo subjects, censorship and low-quality information are common in search results. In two papers I recently published at CSCW-both led by Kaylea Champion-we presented a series of analyses showing how taboo shapes the process of collaborative knowledge building on English Wikipedia.

The first study is a quantitative analysis showing that articles on taboo subjects are much more popular and are the subject of more vandalism than articles on non-taboo topics. In surprising news, we also found that they were edited more often and were of higher quality!

Short video of Kaylea's presentation of the work given at Wikimania in August 2023.

The first challenge we faced in conducting this work was identifying taboo articles. Kaylea had a brilliant idea for a new computational approach to doing so without relying on our individual intuitions about what qualifies as taboo (something we understood would be highly specific to our own culture, class, etc). Her approach was to make use of an insight from linguistics: people develop euphemisms as ways to talk about taboos (i.e., think about all the euphemisms we've devised for death, or sex, or menstruation, or mental health).

We used this insight to build a new machine-learning classifier based on English Wiktionary definitions. If a 'sense' of a word was tagged as euphemistic, we treated the words in the definition as indicators of taboo. The end result was a series of words and phrases that most powerfully differentiate taboo from non-taboo. We then did a simple match between those words and phrases and the titles of Wikipedia articles. The topics were taboo enough that we were a little uncomfortable discussing them in our meetings! We built a comparison sample of articles whose titles are words that, like our taboo articles, appear in Wiktionary definitions.

In the first paper, we used this new dataset to test a series of hypotheses about how taboo shapes collaborative production in Wikipedia. Our initial hypotheses were based on the idea that taboo information is often in high demand but that Wikipedians might be reluctant to associate their names (or usernames) with taboo topics. The result, we argued, would be articles that were in high demand but of low quality.

We found that taboo articles are thriving on Wikipedia! In summary, we found that in comparison to non-taboo articles:

• Taboo articles are more popular (as expected).
• Taboo articles receive more contributions (contrary to expectations).
• Taboo articles receive more low-quality contributions (as expected).
• Taboo articles are higher quality (contrary to expectations).
• Taboo article contributors are more likely to contribute without an account (as expected), and have less experience (as expected), but that accountholders are more likely to make themselves more identifiable by having a user page, disclosing their gender, and making themselves emailable (all three of these are contrary to expectation!).

Image of the estimated qualiy of articles of the four articles in the second mixed-methods paper. Extreme dips reflect periods of frequent vandalism.

Kaylea attempted to understand these somewhat confusing results by designing a fantastic mixed-methods analysis that sought to unpack some of the nuance missing in the quantitative analysis by delving deep into the "life histories" of four articles on English Wikipedia: two on taboo topics related to women's anatomy (Clitoris and Menstration) and two nontaboo articles chosen for comparison (Cell membrance and Philip Pullman).

Although the findings from the analysis can be difficult to summarize succinctly (as with many qualitative studies), we showed how the taboo example articles' success was hard-won amid real challenges and attacks. The paper describes how challenges were overcome through resilient leadership, often provided by a single dedicated individual. The paper provides a template for how taboo can be-and frequently is-overcome by dedicated Wikipedians in ways that provide useful knowledge resources in real demand.

For more details, visualizations, statistics, and more, we hope you'll take a look at our papers, both linked below.

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The full citation for the papers are: (1) Champion, Kaylea, and Benjamin Mako Hill. 2023. "Taboo and Collaborative Knowledge Production: Evidence from Wikipedia." Proceedings of the ACM on Human-Computer Interaction 7 (CSCW2): 299:1-299:25. https://doi.org/10.1145/3610090. (2) Champion, Kaylea, and Benjamin Mako Hill. 2024. "Life Histories of Taboo Knowledge Artifacts." Proceedings of the ACM: Human-Computer Interaction 8 (CSCW2): 505:1-505:32. https://doi.org/10.1145/3687044.

We have also released replication materials for the paper, including all the data and code used to conduct the analyses.

This blog post and the paper it describes are collaborative work by Kaylea Champion and Benjamin Mako Hill.

23 Mar 2026 9:33am GMT

Review: Dark Class, by Michelle Diener

Series:	Class 5 #5
Publisher:	Eclipse
Copyright:	2022
ISBN:	0-6454658-2-8
Format:	Kindle
Pages:	349

Dark Class is the fifth novel (not counting the skippable novella) in Michelle Diener's Class 5 romantic science fiction series. As with the previous novels, this follows romance series conventions: There are new protagonists, but characters from the previous books make an appearance. It's helpful but not that necessary to remember the details of the previous books; the necessary background is explained enough to follow the story.

By now, series readers know the formula. Yet another Earth woman was secretly abducted by the Tecran, encounters a Class 5 ship, and finds a way to be surprisingly dangerous and politically destabilizing. This time, Ellie has been mostly unconscious since her abduction and awakes in a secret Tecran base after the Tecran have all been murdered. There is a Class 5 AI involved, but not a full ship; instead, Dark Class picks up (or, arguably, manufactures) a loose end from Dark Minds. Other than that break from the formula, you know what to expected by now: a hunky Grih, a tricky political standoff, a protective Class 5, a slow-burn romance, and a surprisingly capable protagonist who upends politics through plucky grit and refusal to tolerate poor treatment. Oh, and a new selection of salvaged clothing and weapons to make Ellie beautiful and surprisingly dangerous.

If you are this far into the series, you probably like the formula. That's my position. I don't care about the romance, but something about the prisoner to threat evolution of the kidnapped protagonists and the growing friendship with an AI makes me happy. This is not great literature, but it is reliably entertaining with a guaranteed victorious protagonist and happy ending, making it a comfortable break from more difficult books with emotionally wrenching scenes.

Dark Class is one of the better executions of the formula because it has long stretches of my favorite parts of these books: exploration of mostly-abandoned surroundings for neat gadgets while the AI and the protagonist slowly build a relationship of mutual respect. This book has bonus drones with minds of their own and an enigmatic alien spaceship that provides a fun mid-novel twist. The Tecran and the Grih repeatedly underestimate Ellie and are caught by surprise at dramatically satisfying moments. It's just fun to read, and I save this series for when I need that type of book.

As with the other books of the series, Diener's writing is serviceable but not great. She repeats herself, uses way too many paragraph breaks for emphasis, and is not going to win any literary awards for prose quality. The series is in the upper half of self-published works, and I've certainly read worse, but either the formula will click with you or it won't. If it doesn't, the prose is not going to salvage the book.

There is some development of the series plot, but it's mostly predictable fallout from Dark Matters. This book is mostly tactical and smaller in scale. I am a little curious where Diener is going with political developments, since the accumulated Earth women and Class 5 ships are in some danger of becoming a sort of shadow government through sheer military power, but I'm dubious this series will have enough political sophistication to dig into the implications. It's best enjoyed as small-scale episodic wish fulfillment for female protagonists, and that's good enough for me.

If you've read this far in the series, recommended; this is one of the stronger entries.

Followed by Collision Course, which breaks the title convention for the series.

Rating: 7 out of 10

23 Mar 2026 4:31am GMT