In a world where everything is tracked and kept forever, like the world we're for some reason building, you become hostage to the worst thing you've ever done.
I really like this quote becaus it clearly shows the main risk of data collection, since it makes mistakes so permanent and removes ability for people to move on. The argument becomes a little more personal which makes it easier to understand.
no one has been able to predict what the actual visualization should look like.
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.
it is not possible to know what a visualization would look like in advance,
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!
they do not tell us anything new
This is the main problem the author is responding to , the idea that digital history doesn’t produce new insights.
By all means, do that, but don’t pretend you are the sole arbiter of what your data is for. You aren't!
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.
If you go searching for patterns in the data, you'll find patterns in the data.
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
Machine learning is like a deep-fat fryer.
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.
And this time it's not the government, but the commercial Internet that has worked so hard to dismantle privacy.
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.
Just as we need more nuanced data models for time, we find ourselves faced with a pretty limited palette of options for depicting important structures of power, like gender and race.
This shows how data can oversimplify pretyt complex things like identity.
This is not a perfect analogy, but imagine that someone called your family photograph album a dataset
This example makes it clear why calling everything “data” can feel pretty wrong becae it removes meaning and context.
“Let’s see your data.” “Data?” they say. “Oh, I don’t have any data.”
I foudn this interesting because it shows how humanists rely more on interpretation than structured datasets.
We do, however, want to question the notion that “literary greatness” can be measured by the number of physical copies of a book held on library shelves.
This feels like the main takeaway. Just counting copies doesn’t fully capture a book’s impact, meaning, or cultural importance.
and we can see that over 70% of the novels were written by men.
This statistic clearly shows gender imbalance. It suggests that ideas of “greatness” have historically ALWYSA favored male authors.
But power over a platform requires knowledge of what it purveys. And where we collectively lack that information, we lose the capacity to steer creation’s course.
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.
The platforms, in other words, like to encourage advertising by telling their own stories about what’s trending.
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.
And yet, as academics—and by extension, the general public—we don’t know what they are actually consuming: the types of content that they are viewing, liking, sharing, or commenting on in the largest quantities.
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.
Platforms are the new publishers
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.
Corporate distributors for public libraries are, in fact, already swooping in and capitalizing on the need for data-driven diversity audits
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.
“A poor sales history on BookScan often results in an immediate pass,” Boys said.
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.
I went looking for book sales data, only to find that most of it is proprietary and purposefully locked away.
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.
Given the size of many archival collections, only the most important materials can be described at item-level
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.
Who is this for?,
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.
If everything is digitized, regardless of metadata or image quality, the resulting hoard would solve a host of issues.
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.
The top four were all musicians whodied
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.
the spike in pageviews after a celebrity’s death can often overshadow that of other major events, even a presidential inauguration
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.
Let’s look at the percent increase in pageviews compared to a typical day.
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.
Spaces :
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.
So here is my list of things that should not appear in file names:
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.
On most days I move between Mac OS X (HFS+), Windows XP (mostly NTFS, some FAT32), Windows 2003 (NTFS), FreeBSD (UFS/UFS2) and Linux (pick one).
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.
Okay, all kidding aside, having goofy file names can make life miserable.
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.
Thus users might search and find relevant works, but not be able to view them.
This shows how early digital library design ignored user experience, making access frustrating instead of helpful, as it hsould actually be.
These six well-funded projects helped set in motion the popular definition of a "digital library." These projects were computer science experiments, primarily in the areas of architecture and information retrieval.
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.
But it would be a mistake to see digital libraries as primarily providing ways to access material more quickly or more easily, without having to visit a repository across the country.
Besser argues that digital libraries are about new possibilities, not just things like speed or convenience.