Fluency change

So far in LS 560, I have done mostly web design. My IT fluency levels haven't changed substantially, but there is a marked difference between what I know I can do in theory, and what I can do in practice. I had a working knowledge of html years ago, but my skills have atrophied from lack of use, so the ability to put them to use again has been exhilarating and informative. I am relieved to find that html has not changed much since I designed my first webpage.

One new skill for me is the use of style sheets, which I had never done before. For me they are still in the range of theoretical rather than practical, but I'm working on changing that and becoming more familiar with how to utilize them effectively. The main challenge for me so far has been figuring out the syntactical differences between html and css, so that I can determine how to map concepts and tags from the html in the site design itself over into the style sheet.

Additionally, I'd never used any of the meta-data tags in my html coding before. I had never heard of them before when I had my original experience with web design. I don't know if that's because they weren't en vogue when I learned to build websites, or if I was simply too much of a self-taught amateur to be aware of them. I remember hearing about them as a way to manipulate google years after, and controversies that caused google to change the way it reads metadata, but I never figured out the specifics of how such tags are indexed or considered. I would still consider myself an amateur in this area, and am still trying to determine some of their precise practical applications.

Some of html has actually simplified with html 5, so my web design has actually become a bit easier than it used to be. In addition to exploring style sheets and meta tags, I am using columns and tables for my site, which I had some previous experience with, but have fleshed out my competency a bit. If I have time before the assignment comes due, I am also looking forward to exploring a little java, which I had barely gotten my feet wet in years ago doing mouse overs.

IT Fluency

A new semester, and a new blogging adventure! This blog is called Allie's Kitchen, because Allie is my constant screen name and nick on the wide world of the internet, and because for most of my life I've been professional cook. My kitchen at home is my safe space, the space I feel most comfortable, and usually the space where I do the most thinking and decision making, and I wanted to bring that feeling and sense of thoughtfulness to my librarian blogging. It's a combination of my internet self, who is focused on constant learning and interaction, and the thoughtful composed part of myself that does most of my critical thinking. I feel that my librarian self would ideally be a balance between these two facets.

Currently, I am working as a tax preparer during tax season, while on hiatus from food service due to health issues I'm sorting out. I live in Huntsville, with my girlfriend and my very thoughtful six-year-old daughter, as well as four cats and one dog. (Help me please lol.) My interests in library studies include user-interface system design and public library community outreach issues that can be aided with technology. How we search, what we search for, who gets to search, and why we search for the things we do the way we do are all interesting research questions for me. One of my longterm goals is to develop the skills necessary to help build a collaborative search interface built by librarians for libraries, which would both reduce library reliance on third party system, and allow for greater customization to serve diverse population needs. We share the workload when it comes to authority records and collaborative call number assignments, why not share the work on our own search algorithms too?

As far as my proficiency with technology, I have done quite a bit with html and database design and maintenance, but most of my experience is rather dated, and forgotten from lack of use. As far as skills that are less rusty, I'm comfortable with social media and proficient in the use of internet information search tools. Having not grown up with computers, I am fascinated by them, and often approach technology the way I imagine most Baby Boomers do, as a powerful tool constantly both present and necessary in my life. However, like someone of an older generation, I often take a bit more time to intuitively grasp new apps or new technologies. I did not own a computer until I began grad school in Fall, and instead used a cheap tablet which I had modified to behave like a computer in most circumstances. I did not own a smartphone until last Christmas (2014) and now I'm addicted to the user-friendliness of a device I don't have to force as hard to do my bidding for social and research tasks. I'm excellent at figuring out ways to make technology do what I want it to do, but it often takes me longer to figure out the appropriate work-arounds.

Many of my own limitations made me decide to interview my girlfriend, to compare our differences in technology use, because of the many differences I know we share in these areas. For one, she grew up in a much higher income bracket than I did, and has almost always had ready access to modern technology and computers. She does not own a computer, but has had a smartphone almost since smartphones existed, and uses it in the same ways I normally use a computer. She is extremely versatile in more modern forms of technology, but she still winds up coming to me with issues that involve more old-school research skill. It's almost as though her tech savviness skips a generation because of her income level. I find that concept fascinating. Another reason for our differences is that I am a visual learner, and think in words and concepts. I read constantly. She, on the other hand, is slightly dyslexic, and not as comfortable reading when she needs to find information. She is more likely to try to find a video or sound bite for what she needs to know, and would rather watch Netflix than read a book, no matter the subject. This makes for quite a few differences between us, even though we are the same age, and I thought exploring these differences could prove educational.

I focused my questions more on practical applications of technology, and how she would go about solving a technological problem. Knowing that her smartphone is the only means she normally uses to use the internet, first I asked what steps she would take if she lost her phone. She answered that after having a good cry about it, and provided the phone was truly lost and she couldn't find it, she would borrow a phone to call her service provider and have the number turned off, as well as try to log onto facebook and let everyone know that her phone is lost. She did not seem aware of the fact that service providers can often track lost and stolen phones. She would then attempt to get some kind of replacement phone, even if it wasn't a smartphone and have her phone number transferred to it so she could stay in contact.

My second question posited a meeting with friends, and I asked how she would go about communicating and coordinating meeting times and places with others. She said that for pre-planned event she would create a facebook event and distribute invitations electronically. This is a technology feature that I have only recently become aware of, so she seems more proficient than me in this aspect. For a smaller group or a more casual event, she would call and text others to establish contact, which is understandable considering her main means of connecting is, besides all else, a phone.

I also asked about how she would go about performing a research task for work, or doing an online job application, knowing that these things are notoriously difficult to do using a smartphone, and asked about how she would deal with such barriers. She said that her primary research method would be to use google to find appropriate resources, and had knowledge of where to look for job links on employer websites. Faced with difficulties, however, she admitted that she would probably turn to me for help, or go to the public library to use a computer for job applications or an encyclopedia for research. When I asked, she was not aware of the existence of online encyclopedias, or how to use many of the library's research tools. She knows how to copy and paste a resume into an application, but is not aware of how to use attachments or store documents online.

I found these differences extremely illustrative of the differences between our technology habits which illuminate our divergent ways of thinking and experiencing technology on a regular basis. Whereas I'm just discovering many social media tools and smartphone apps and technology, my girlfriend is far more fluent in those types of technology, whereas she seems lacking in other computer-based skills that I consider fundamental to the way I use technology such as online data storage and research techniques. I feel that this is illustrative not only of our differences in terms of what technology we grew up with and what means we have each traditionally used to access resources, but also indicative of the differences inherent in our personalities and learning styles, as well as what kinds of resources we are likely to seek out. She mentioned that rather than researching a particular subject, she is more likely to wait for information to show up on her facebook feed, whereas I often seek out information through other means, and I consider the passive vs. active learning implications an interesting concept for possible further exploration.

Article Response for Lecture 14 - Shirky

Shirky, C. (2005). Ontology is overrated: Categories, links, and tags. Blog post.

                Shirky states that many of our strategies for attempting to categorize resources in a web environments are holdovers from a time when different categorization strategies made sense and that our assumptions are outdated. She argues that hierarchical classification is extremely useful when it comes to small numbers of things to categorize, when those things have definitive markers, making them difficult to misclassify, and when both the creators of the hierarchy and its users are subject experts. She uses the examples of the periodic table and the psychology DSM as examples when hierarchical structure works well, but posits that as human knowledge continues to grow, especially as we look at the extreme growth of web-based knowledge, these hierarchical structures become less useful.

                For one thing, the “aboutness” of a work, which she refers to as its “isness” or essence, isn’t a concrete concept, but is variable with context. A number of people may think of the same concept from a multitude of viewpoints and thus use a multitude of terminologies to refer to it. Additionally, if all users of the system are not experts on not only the subject, but the hierarchical scheme involved, it will prove difficult for them to find information in a large system. The burden of needing to not only read the minds of all potential searchers, but predict how they will continue to search in the future, is too much for catalogers to maintain in a large system.

                Because of the broadness of web information, none of our current limited classification schemes are universal enough for the task. The author specifically demonstrates several biases inherent in all classification schemes, from Soviet over-classification of Communist literature, to preferential classifications for Christianity in Dewey’s scheme, to geographical preferential treatment given to Western thought in LC classification. These biases arise because we are not truly attempting to classify all knowledge, but rather to solve a concrete problem. These classification schemes are all designed to classify the book in hand, and organize the items in a collection. If the items in the collection have a bias toward Western thought, since we reside in an English-speaking country, then the classification system designed around them will necessarily develop such a bias. Bias in hierarchy is unavoidable.

                Shirky argues that we have forgotten that there is no shelf for online resources, which is why when Yahoo initially began compiling internet pages, it created a hierarchical system, and assigned a “shelf” to each group of links in an antiquated fashion. Pages need not be limited to a single category of knowledge the way physical items are, and may be linked to from anywhere. When Google came along, it took a different approach and uses a post-coordinated collocation system when the user searches, rather than a hierarchical model. The author argues that this leads to greater success in a web-based environment.

                The potential of non-hierarchical systems of organization, such as folksonomic user tagging is a lessening of binary thinking. A resource is not simply either one thing or another thing, nor is it an aspect of a thing within a broader category, but it can be multiple equally represented things at the same time. This crowd-sourcing form of information management is often effective, if also at times inelegant. It allows the user to decide what is important or relevant, and offers filtration only after publication, a complete reversal of the print publishing industry. The lack of controlled vocabulary allows users to maintain the nuances inherent in their terminology, rather than squeezing their concepts into over-arching categories which include tangential, or even unrelated subjects.

                I personally find folksonomies, and user-generated classification fascinating because of the mathematics involved. A majority of users will tag something as what it is, and use various terms to do so. With a great enough volume of user tags, irrelevant subjects are edited out, or decreased in relevancy to the point that they do not influence the user perception of the subject. However, I would caution that ‘rule of the mob’ is not always fair or just, and it is possible to mobilize a large number of users to the detriment of a given link or subject. Online harassment makes this possibility quite clear. Additionally, when knew knowledge is presented it needs initial tags in order to gain legitimacy and categorization. New knowledge is a problem, when the idea is that the greater the number of taggers, the greater the accuracy. When a subject is new, it has few tags, which means decreased accuracy.

Article response for lecture 11 - Rafferty

Rafferty, P. (2001). The representation of knowledge in library classification schemes. Knowledge Organization, 28, 180-91

The main argument of this article is that power is no system of classification is without some level of bias. This is important because social power and structure are conveyed through classification schemes. The ways in which libraries categorize and classify knowledge mirrors the way society views that knowledge. Library classification schemes are rooted in the practical applications of how users search for and use knowledge. Controlled vocabularies and hierarchical structures attempt to optimize usability. However, the question remains, for whom are such systems optimized?

When a subject is defined as a main class, it becomes of primary importance, within which subclasses are secondary, and further divisions tertiary, etc. So, the structure itself is necessarily fraught with bias. They simultaneously dominate over a given piece of information, forcing it to conform to a given structure and organization into which it may not easily fit, and enable easier searchability and greater open access to varied ideas. Therefore, organizational schemes can both maintain and subvert a given paradigm, often at the same time.

Because classification schemes are built on other existing classification schemes, and prior knowledge, they are necessarily biased by what came before. For example, notational language and controlled vocabulary must, by necessity, be exclusionary. Those who don’t use the right terminology or correct notation are considered inferior. How we place and organize records is influenced and controlled by how we feel and think about them and their subjects. There is a necessary subjective bias.

Many organizational schemes were originally defined by religiosity, with God at the top of the hierarchy of classification, and the dominant religion of the culture considered the standard default in discussions of religion. Likewise, a schema which places academia at the center, and utilizes the organizational schemes derived from various disciplines of academia, has the advantage of taking into consideration the information organization that a majority of users will utilize. However, this presents the bias of illegitimizing sources external to academia, particularly Western academia, and promulgating a specific view of what is important and what is not, based on a particular world-view.

What is classified and what is not marks a boundary for what is important knowledge and what it not. This defines the self vs. other, and demarcates the boundary for what matters to society. Libraries as social institutions are completely involved in what information, and what sources, are legitimized. For example, fiction is often devalued, until it happens to sell enough copies, at which point it is accepted by academia as a social signifier. So, classification makes its way from libraries to realms of social thought and vice versa.

Article Response for Lecture 10 - Underwood

Underwood, T. (2014). Theorizing research practices we forgot to theorize twenty years ago. Representations 127, 64-72.

                When we search large sets of data, we run into the problems of confirmation bias. That is, if we search for a question that we already believe we know the answer to, we are likely to find, whether or not it is correct.  This is because given a large enough dataset, it is easy to find at least one example of any concept no matter how fallacious. The number of example that we need in order to prove a point depends on the size of the dataset, and few researchers know the size or scope of the datasets they search. Researchers come to the search process with preconceived notions of what is true, and what they need to find. They come with very specific questions, and want to find a particular answer that they intuitively believe is correct. Data mining of large sets of data is often a fishing expeditions to try to find the select few examples that confirm are already preset notions of what is true, particularly in the humanities and with the studies of linguistics.

                Additionally, ranking by relevancy can filter out any information which disproves our preconceived notion. When we use search engines, especially full text ones, algorithms show us immediately what we search for, regardless of whether the subject is correct or not. More difficult than the issue of synonym exclusion, is the idea that every facet of data that does not conform to the language of our search, and thus our bias, is filtered down, so we are less likely to see any contradictory information.

                Scholars of the humanities often search for certain keywords, using the distributional hypothesis. The distributional hypothesis implies that the “meaning of a word is related to its distribution across contexts.” This is like Wittgensteinian game theory, wherein meaning is determined by usage. While this approach has merit, seeing that a given word is associated X number of times with another given word, does nothing to inform the searcher about its usefulness without also knowing what other words may be associated with it more frequently, what context the associations are in, and how large the dataset is.  These considerations are often omitted from scholarly research because of an over-reliance on search algorithms to ascertain value and truth.

                An algorithm should not be trusted to automatically confer authority and relevancy, because algorithms are not simply blunt instruments, tools hammer datasets into shape, but come with their own inherent biases and limitations. Most algorithms, however, are proprietary, and thus not subject to public scrutiny of its mechanism, so we have no way to contextualize the search process, and provide meaning to datasets of associated search terms. Computer scientists are working to fix this by using topic modeling to more clearly define associations of terms into clusters, so that words can be associated with other words in given contexts. This process can reveal subjects and ideas we didn’t know to look for in our initial search, and contribute more effectively to scholarship, rather than simply confirming what we already thought was true.

Article Response for Lecture 9 - Rotenberg & Kushmerick

Rotenberg, E. & Kushmerick, A. (2011). The Author Challenge: Identification of the Self in the Scholarly Literature. Cataloging & Classification Quarterly 49(6), 503-520

                This article began as an effective examination of the problems with attribution of scholarly scientific publications, and then submitted a given solution. The first half distinguished attribution as a necessity for allocation of government and grant funding, as well as for tenure decisions for individuals. However, names can be common, and individuals can often have similar names, making attribution tricky. Additionally, scientific scholarly output is increasing at a rapid pace, adding more common names to the jumble. Non-traditional forms of publication, such as web published pieces, and those in three-dimensional models instead of writing, proliferate the scientific landscape.

                Several different international organizations are currently working on name disambiguation, in which authors themselves claim their work. The authors suppose that no one single company can cover all disambiguation in the world, so disambiguation must necessarily be a collaborative effort. The international entities linked to one another, with the authors supporting each entity in a pseudo-folksonomic fashion can create a web of disambiguation. The web particularly discussed was Web of Science and its particular disambiguation community ResearchID.

                Web of Science uses an algorithm to collocate works by a single author. The difficulty mentioned with algorithmic disambiguation within the Web of Science search engine is that it collocates incorrectly whenever authors don’t stick to a strict subject matter, or when authors change names. ResearchID is an attempt to fix this difficulty. The feedback system was mentioned as a critical component to disambiguating correctly, because human users can disambiguate in such cases better than the algorithms, without the added cost in employee searching.

                ResearchID offers identification numbers to each individual author, as well as citation metrics to allow authors to disambiguate themselves. It allows interactive maps of collaborators and citations to analyze an author’s geographic spread of knowledge. In many programs and communities, it has been implemented to help disambiguate authors from inventors and principle investigators. Instead of relying solely on the metadata attached to the article itself, it pulls author data from grant databases and other sources, and allows self-disambiguation

I find it significant that the authors do not see fit to mention NACO, or indeed any LC disambiguation, but only lend credence to disambiguation systems done by authors themselves rather than catalogers. While I agree with their assessment of the value of folksonomy-type disambiguation, I find it disingenuous not to at least mention a divergent way of doing things, and possible criticisms.  The second half of the article seemed more and more like an advertisement for Thomson Reuters projects and products as I continued reading. While the authors seem to believe that further interoperability is the sole goal of future projects, I find it significant that no mention is made of author fraud. I would think that with a folksonomy-type system this would become an issue, or if it is not, is at least worth a mention.

Article response for lecture 8 - Knowlton

Knowlton, S.A. (2005). Three decades since prejudices and antipathies: A study of changes in the Library of Congress Subject Headings. Cataloging & Classification Quarterly 40(2):123-45.

            This article addresses biases inherent in subject cataloging, and assesses modern improvements, and how well the previous objections had been satisfied. It points out a philosophical balancing act between the stated goals of search optimization and universal bibliographic control. Subject categories are designed to enable ease of searching, and allow users to find resources by the most common term, or the term they are most likely to search by. However, whenever one presumes to imagine what a user will search under, or what the most common term might be, which allows personal biases and prejudices to play a role in cataloging. There is a danger of normalizing a single experience, and overwhelmingly the average viewpoint is white, male, heterosexual, and Christian. This bias runs the risk of stigmatizing any group that does not fall within those specific norms, and create subject headings which make resources harder to find for certain users.

            Specifically, Sanford Berman published one of the first widely regarded critiques of bias in Library of Congress subject headings. Since its publication, many of the modifications suggested by Berman have been at least partially implemented. In the past several decades, terminology has changed, which to some degree necessitated different changes from those Berman suggested, accounting for some of the disparity between his recommendations and actual changes. Additionally, the vast majority of his recommendations for subject changes related to African-Americans and women have been implemented, perhaps indicative of the social climate and movements of the times between then and now.

            One subject area which has remained stubborn is religion. Religious subject categories without qualification are assumed to be Christian. Thus, religious subheadings which relate to Christianity are not qualified as such. While some cases of this could be considered exclusionary toward other religions, I would argue that most of those listed are subjects particular to Christianity, and not subject to confusion.  Obviously the term ‘God’ could be construed in many different religions, but other subjects such as ‘Virgin Birth’ is mythologically associated with Christianity, and not necessary to disambiguate. Such unnecessary disambiguations may account for some of those not addressed.

            Other than religious subjects, I did notice that two other types of subjects were not addressed. Subjects under ‘poor’ and many of those regarding poverty and economic disparity was not disambiguated or made into less offensive categories. Perhaps this is because of the lack of emphasis on socio-economic disparity until very recently in the history of social justice. Likewise, several aberrant subject headings involving indigenous populations were not altered. Similarly, social justice issues in US culture have not emphasized international themes historically until very recently, and so many of these headings are likely still catching up to culture.

            Ultimately, I think the alterations to LC subject headings since Berman’s original study have been fairly adequate, and have stayed abreast of modern social attitudes as well as can be expected for a complex cataloging structure. However, the age of the original study makes me wonder if there have been more modern ones reassessing LC subject headings to see what a more modern take on biases would reveal. If we are still considering a decades old study as a litmus strip for innovation, it’s unsurprising that LC subject headings pass the test.  I think we could use a more modern litmus test.