This relates to the prev reading and supports the main argument. The results are not obvious, even if they feel like that after seeing them.

This is an imp point. If we couldn’t predict the result before, then it doesn’t make sense to say it was “obvious” after seeing it!

This is the main problem the author is responding to , the idea that digital history doesn’t produce new insights.

Liekd this because it challenges control over data. It suggests that once data is shared, others should be able to use it in different ways.

This suggests that data analysis can be misleading becaue you might find patterns just because you’re looking for them, not because they have any significance

I lovw this metaphor! It suggests Ml can be applied to anything, but that doesn’t mean it’s always meaningful or appropriate to do it.

This stood out because it shows how data collection today is tied to surveillance. It makes me think about how using data isn’t just neutral but it’s connected to power and privacy issues.

This shows how data can oversimplify pretyt complex things like identity.

This example makes it clear why calling everything “data” can feel pretty wrong becae it removes meaning and context.

I foudn this interesting because it shows how humanists rely more on interpretation than structured datasets.

This feels like the main takeaway. Just counting copies doesn’t fully capture a book’s impact, meaning, or cultural importance.

This statistic clearly shows gender imbalance. It suggests that ideas of “greatness” have historically ALWYSA favored male authors.

This seems like the main point. If we don’t know what content is most popular then we don’t really have control over what shapes tthis online culture.

This suggests that “trending” content may not just reflect what people like, but what platforms want to promote. That makes popularity feel less organic and more forced.

This stood out to me because we spend hours on these platforms, but we don’t have a clear picture of what is most viewed in general. because everyone is on such different sides of the app there is so much content we never see pr even know it exists.

This feels like one of the main arguments of the article. Social media companies now shape what becomes popular in the same way somethig liek TV networks used to. That changes who has cultural power.

It’s interesting that even diversity efforts can somehow just become business opportunities. This makes me think about whether companies are solving problems or just tyring to profit from them.

This makes publishing sound very numbers-driven. It seems like one book that didn’t sell well could follow an author forever, almost like a permanent record.

It’s surprising that data about something as common as books is locked away. If researchers can’t access this information, it limits how much we can really understand about so many thins.

This made me wonder how archives decide what’s “important.” She says only “important” items get detailed descriptions. That means archivists decide what gets more attention. If something isn’t described well, people might never find it.

I like this question, because it makes me think about the fact htat archival rules seem designed to make institutions run smoothly, not necessarily to help the communities represented in the records.

She’s questioning the idea that putting things online automatically fixes access problems. Just uploading images doesn’t fix things like racism or bad systems.

There’s clearly a pattern between unexpected deaths and attention spikes in media. But the article doesn’t really explain why that happens , it just shows the trend. Definitely makes you realise how shock and tragedy keep people engaged in the media longer.

This just claims celebrity deaths can take over public attention even more than political events. That shows a lot about what people prioritize in media and it really makes me question what drives public attention.

Using percent increase makes the increase look a lot more more dramatic. Someone with low usual traffic(Roy Halladay, etc) could look huge just because their baseline was small and thisshapes how we interpret actual impact.

The issue with spaces is a good example of how a normal human habit like just using spaces becomes a technical problem. Something that comes so naturally to us when naming a file turns into extra work in command-line environments, which j shows a big difference between human language and systems.

The goal of this piece is defintiely more practical than academic. It works like informal documentation meant to help readers toward better file-naming practices.

The author baiscally supports his point by describing his experience working across many operating systems. This shows that the issues he mentions come from actual technical experience.

The author’s main argument is that file naming is not just a personal preference. Poor file names create like actual technical problems, especially when files are shared or moved between different systems.

This shows how early digital library design ignored user experience, making access frustrating instead of helpful, as it hsould actually be.

Early digital libraries were primarily defined by technological experiments rather than by actual library needs. They prioritized system development over user support, research usability, and protecting information over time.

Besser argues that digital libraries are about new possibilities, not just things like speed or convenience.