Welcome to Library Work handout

The Aus GLAM Blog Club theme for this month is “Secrets”. I’m very excited because I have been thinking about secrets and industry knowledge this year.

When I was working as an IT professional on projects for libraries, I found that I kept giving IT contractors very basic introductions to what libraries are. Not the big things that we’d normally think are important for newbies to learn, but the basics. Our assumed knowledge. Things that we take for granted, but that sound SUPER WEIRD if people hear you talking about it without any context. In other words, things that are secrets because we think that everyone knows about them.

If we omit – forget to share – some of this basic knowledge then we can end up with misunderstandings and huge projects that fail or run over time and budget. It really pays to make sure everyone is on the same page when we start working together across different disciplines.

This draft is still a bit raw, but I wanted to share something I’ve been working on over the last couple of months. I had about ten pages that I slowly edited down to 4 (around 6 without the table formatting).

For now I have uploaded some different versions. I’m not an expert at formatting for screen readers, so I’ve made longer versions without tables, and shorter versions with tables. There’s a .odt open document and a .pdf version of both, and they’re all Creative Commons Attribution (CC-BY) so that you can use them any way you like. I made this all in my off-work hours, so that I own all the intellectual rights and I can share it with others outside of my workplace. If you want to share it too, I recommend checking with your boss to see what your work contract says.

Library-handout-basic pdf

Library-handout-basic odt

Library-handout-tables pdf

Library-handout-tables odt

If you like this and make another version, let me know! I’d love to link to other versions, and learn from what you find useful.

To make the CC license super clear, I am an enemy of the unpaid extra labour that librarians do in the name of professional development. I am giving this away for free because I can afford to, and I think it’s worth sharing with everyone. If you put hours of work into adapting this or customising it, and you want to charge money for that work, I am 100% supportive of you.

I’m a little shy about posting this now, but I hope that as rough as they are, my drafts are useful, especially to smaller libraries who can’t always afford ongoing IT staff or the time to train them.

Endless diagrams

December’s Aus GLAM Blog Club topic is “End”. I’ve been working on system relationship diagrams and training documents this month, so the word “end” makes me cringe. I’ll try and explain with my relaxed, holiday brain… I think I switched off the moment that I left work for the year.

The “end” of a library system is theoretically the outside edges of all the little systems that we use. If you imagine your catalogue database as a flowchart with shapes representing each system, there will be systems with fewer connections to other systems towards the edges… and some sides of the shapes that don’t have any connections going outwards.

(I apologise for my messy sketches. The colours mean nothing, I just like my coloured pens. Also, this isn’t my current library, it’s an imaginary library, hopefully generic enough to be similar to yours.)

To say that we’ve found our edge there is being a bit dishonest, because most libraries have relationships between their systems – or staff – and external systems. Libraries Australia, WorldCat, or library vendors and journal publishers send in metadata to be incorporated into our libraries’ systems… those are the obvious ones. Think deeper. SurveyMonkey or other sites that somebody in one team at your library has done some user engagement through. Google Docs or DropBox being used by your staff even if it’s against your content storage policy.

Now, think of the “ends” of those systems, especially cloud ones. The end is an ever-shifting gradient rather than a nice, clean line. The more you understand the different risks and dependencies of all these systems and how your library uses them, formally and informally, in their workflows, the more impossible it becomes to explain. It’s often easier to pretend that the systems we don’t own (or pay licensing fees for) fall outside of our overall library “system”. It is also easier to pretend that the on-site hosted software in our server rooms marks the end of our ability to control our data – and thus our responsibility.

It’s awful when I’m working as a test analyst in my library, because it means that “End to End” testing can never realistically reach all potential end points. There’s too many other libraries who are part of this huge and glorious mess.

I say mess as a test analyst, but as a librarian I love it. We are one of the few professions where we both act as, and serve, users of a wide range of systems. From analogue microfilm readers through to e-resources, we work hard collaborating silently and invisibly with librarians all over the world… at least, those who speak, write, and read English… but that’s a topic for another blog.

Every time I talk to people at my library about system relationships, and frame the systems in relation to that person’s daily work, they look at me with awe and horror… but I built my knowledge by drawing shapes in a graphics tool – you could just as easily use PowerPoint or Paint or coloured pens like I did today – and draw lines to represent all the connections.

I think every librarian should have a mental or digital mud-map of their systems and how they relate to external systems. You don’t need to be perfect, it will always be incomplete, because it will always be changing and because sometimes you won’t be able to know what everyone in your library is using. But the more you understand all the connections that are outside of your library’s control, the better placed you are to advocate for your readers needs and to demonstrate the value your services add to the world.

Personally I have found that the better informed my coworkers are about the ways the systems that they work with relate to other ones, the easier they find it to use them. A lot of people get anxious when they don’t know why something is broken, or how often to re-try exporting data to a different system, or who to ask for help when two systems that should be connecting just… aren’t. Knowing those points of connections and how everything fits into a grander scheme of library work and metadata can be really reassuring.

Though to be honest, I think that it can also just be fun to play around and do this, so I use every chance I get to do it!

On reflection, the “end” to your library system is probably the relationships between all the systems that your readers might need to help them use your collection, whether you own them or rent them or simply link to them. And sometimes you can’t imagine how deep that might go until you begin exploring and mapping them out.

Appetite for change

I drafted this for Aus Glam Blog Club’s November topic, “Change”. And then I chickened out of posting it, because my thoughts were this looping ramble and I wasn’t sure if they were worth sharing with the wider world.

Today at work there was a discussion about expertise versus efficiency of scale; of hand-crafted bibliographic metadata and the hidden unpaid labour of crowdsourced text corrections for digital content… about the opportunities and risks that come from open source and commercial machine learning databases that automate a deeper level of content than has ever been available for full text search before.

I feel like these are bubbles of feelings inside me, and they are still growing and building up pressure. One day they will pop and I’ll be able to look back and make sense of them all. But for now I’ll gather my courage and post what I wrote, and maybe people will disagree with everything I say and get mad and write their own thoughts down, and we’ll get more discussions going in the GLAM sector.

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November’s Aus Glam Blog Club topic was Change. As a cataloguer there’s one topic that I’ve changed my mind on a lot. If you ask me in the morning I’ll have one opinion, in the afternoon I will have another. I just can’t pin down what I think or feel about automated subject analysis.

I’m not sure if this is an interesting format for a blog, but here’s the arguments I have with myself as I change my mind back and forth.

It’s a great idea, right? A machine learning algorithm could be fed a small batch of good records for academic journals and fiction and recipe books, and the ebooks or scanned pages of the content. The machine could learn that particular categories of subject headings in a thesaurus relate to specific types of text or content layout and begin to identify patterns of grammar, vocabulary, and unique keywords or frequencies of keywords that identify the differences between the publications. It would be error-prone at first, but with corrected data and more data being added over time, the algorithm could learn how to apply subject headings consistently (and update them).
This algorithm could keep up with changes in terminology or new ideas in specialist fields, in ways that cataloguers cannot in their day to day work. It could help us avoid problems like LCSH and “illegal immigrants”, where different political groups use different vocabularies and the Library of Congress becomes more vulnerable to political bias.

But it’s a terrible idea! I’ve changed my mind. An algorithm would learn all of the awful patterns of racism, sexism, and oppression that we work so hard to keep out of our collections. We’d be more likely to start the machine learning off with our public domain full-text digitised content which is more likely to contain outdated and offensive language, and have catalogue records with outdated terms. We couldn’t begin from a neutral place since the thesauri we currently use to describe our books all have inherent cultural and political and racial biases in them.
In other words, garbage in… garbage out.

That said, perhaps machine learning could free us from the need to curate and maintain thesauri. Even if it would still be based on content that could have inherent bias, an algorithm could use grammar, syntax, and other patterns to differentiate between pejorative uses of terms and neutral ones. It could be deliberately biased to trust (weight) terminology used by reliable publishers or types of publications… which couldn’t be worse than thousands of distributed human beings with their own political ideologies trying to work with a piecemeal patchwork of subject headings that reflect more about the time they were established, than the topics they relate to.

Although… we’re now in the territory of proxies. We’d be saying that this type of publisher is a proxy for an ideal librarian who can see through her own biases to the keywords that will help the reader find the information that they need. This could work really well for tried and true disciplines like law, cooking, and even tabletop role-playing games. But this won’t work so well for interdisciplinary studies that may display a hybrid of patterns from two disciplines. And if it can’t work well for them, would we see a negative effect of cross-pollution where environmental science and newspapers get grouped together a lot because they share a lot of text about the weather and our climate. When we allow an automatic association between two patterns to stand in for the real and unattainable “aboutness” of a text, we can become very vulnerable to those associations frustrating a lot of users and hiding information in weird places in our databases.

Plus, not all quotations are indented or in quotation marks, so that could get very weird.

Another thought I often have is that for open-access shelf libraries like public, school, and university libraries, it can be really difficult to locate useful related texts together. It often comes down to the call of the cataloguer who processes the book, and in Dewey Decimal there’s a few different ways you can order and build the numbers. Maybe we aren’t ready for machine learning to assign subject headings, but we could use machine learning based on topics and subject headings to shelve books so that more people can find them and libraries can optimise shelf space.

The worst idea of course, the one that I can’t get past, is that the real power of machine learning with subject description or shelf classification is that of changeability. It has the most value if it can adapt and respond as the content of a library changes. But that could mean regular “global” changes for subject headings for nearly all the records in the database, and an equivalent amount of exporting and importing changed data from our shared national databases. I’m not sure that our hugely distributed and global shared catalogue database could cope with that. Remember you can run, but you can’t hide, from WorldCat… I mean, the Blob!
It would take a lot of hardware just to process all of that data. It would take a lot of electricity.

While we could use it as a more static one-off kind of deal, trying to replace the time that a cataloguer spends determining the right subject heading and entering it in the catalogue record, that almost feels like we’re giving up on the quality of our catalogue records and how transformational it could be to improve access for everything. The new records we create are a barely visible speck of iceberg bobbing above the water, while there is a huge width and breadth of records that have come before us.

Even if we only brought the records to life in one library’s local system in this way, I think it would be too expensive in terms of resources.

Then again, this is all just thoughts and feelings. I don’t know anything for sure. It would be worth trying, thinking, planning, to see where machine learning could really take library catalogue databases on an individual library level, and globally. Having bad ideas, getting angry and arguing about them, and trying some of the things that will give us the information that we need to learn how to argue about targeting automated library work to make things better and not simply more or faster.

Carrying our beginnings with us

This is my first blog post in a while, and it’s my first post for the GLAM blog network. It’s oddly fitting that the topic for this month is “Beginning”.

The first thing that came to mind for me was something that I learned way back in 2014 when I took an online Library Advocacy MOOC (which is great, check it out!) It turns out that the views that most adults have about libraries and librarians were formed by their first experience visiting a library (one of our readings was some great market segmentation research from OCLC). People who were more likely to support libraries remembered a positive, personal and friendly experience.

I think that this power of beginning, that first experience, is just as powerful in software. We speak a lot in libraries and IT of “change management” and fear of change, especially in older staff, but people are most comfortable with the software that they learn to master first. It’s why a lot of my older colleagues are actually highly confident with MARC21, text-entry, Boolean searching, and email. I can’t beat them even though I’ve got a reputation for knowing my stuff. I was a teenager when MSN Messenger was still around and even though I adapt to emails at work, I use the office instant messaging system for a lot of my communication with people who are my own age. I am less comfortable with Tumblr, because I started out with LiveJournal and WordPress.
It’s so easy to pass this off as age-related difficulties or a reluctance to learn, but it makes so much sense. Our emotional experiences in our first operating systems define how we react to everything that comes after. It makes me so curious about what comes next, and whether or not it will make me uncomfortable. But it also gives me so much empathy for the people I am designing new software for. I don’t know where they’re coming from; whether they’ve been a Mac or a PC person, or if they’ve only ever used their phones and they don’t know what a mouse is.

When I studied Archaeology I learned something similar, though this was about the dead that we were researching instead of how the world in general perceives archaeology. I was taught that my personal understanding of gender, sexuality, health, and race were all things that I was unconsciously projecting onto the evidence that I was studying. My teachers exposed me to evidence highlighting that things that we think are concrete divisions, are more like fading and speckled transitions. As an example, let’s say that there’s a female-looking skeleton that has been buried with a sword and also a spindle. Was she a warrior? Or are these just gifts from her family members? Did she identify as a man or a woman during her life? Are we looking for other information; do the burial items appear to be used, or are they new and decorative for the burial? Is the grave the same size as others nearby, or is this maybe a double sized burial for two people, one who never came home? There’s no answer to these questions but there’s always a lot of excitement about burials that show unusual gender expressions, and the discussions often reveal way more about our modern ideas of gender and sexuality than the culture we’re trying to study.

I also think, as silly as this sounds, that’s okay. It’s important to accept that we’ll all have these weird beginnings as we set out to try and use something new or find new information. I used to get very angry when I copied a catalogue record and the subject headings reflected a strong personal bias. My strongest reaction was to an American book on domestic violence that had been classified as “infanticide” because it had a chapter on miscarriages and abortion, where it was obvious that the cataloguer had such strong personal feelings on the topic that they wanted to include it. I made some changes to keep it accessible but to remove that personal political bias. At the time I was furious, because it was only one chapter and it’s not appropriate to add subject headings for parts, only the whole, of a book. That’s what contents note fields are for!

But over time, I’ve become far more accepting. That cataloguer has a different beginning to me in their career, and their social context is different. We were both doing the best we could to make that information accessible. Their records will always carry some bias. So will mine. The real trick to becoming a better librarian is looking at yourself as a person and understanding how your knowledge about a topic began. Where you come from. Who you are. And how every single person who walks in through your library’s doors has their own and different beginning.

Libraries are actually very weird places to walk into for the first time. There’s a lot of secret and unspoken rules that are never written down on the signs or in the terms of service. But there’s always new people entering our communities who have zero experience in a library… or zero experience searching in a library… or their experiences are from a different type of library and everything they think they know about libraries is wrong in our building, which can be a really traumatic experience.

I try to keep my beginning and other people’s beginnings in mind all the time whether I’m cataloguing a book, designing a new library system, or teaching somebody how to search for information. It makes me a better librarian, especially when I can set aside my own personal assumptions, approach this new situation with open curiosity, and help somebody find something that they care about.