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Record W2916346768 · doi:10.29173/iq949

Failure as the treatment for transforming complexity to complicatedness

2019· article· en· W2916346768 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIASSIST Quarterly · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEnthusiasmComputer scienceData scienceGeospatial analysisWorld Wide WebPsychologyGeographyCartography

Abstract

fetched live from OpenAlex

Welcome to the fourth issue of volume 42 of the IASSIST Quarterly (IQ 42:4, 2018).
 The IASSIST Quarterly presents in this issue three papers. When you know how, cycling is easy. However, data for cycling infrastructure appears to be a messiness of complications, stakeholders and data producers. The exemplary lesson is that whatever your research area there are often many views and types of data possible for your research. And the fuller view does not make your research easier, but it does make it better. The term geospatial data covers many different types of data, and as such presents problems for building access points or portals for these data. The second paper also brings experiences with complicated data, now with a focus on data management and curation. I would say that the third paper on software development in digital humanities is also about complicatedness, but this time the complicatedness was not overcome. Maybe here complexity is a better choice of word than complicatedness. In my book things are complex until we have solved how to deal with them; after that they are only complicated. The word failure is even among the keywords selected for this entry. Again: Read and learn. You might learn more from failure than from success. I find that Sir Winston Churchill is always at hand to keep up the good spirit: ‘Success consists of going from failure to failure without loss of enthusiasm’.
 From Canada comes the paper ‘Cycling Infrastructure in the Ottawa-Gatineau Area: A Complex Assemblage of Data’ that some readers might have seen in the form of a poster at the IASSIST 2018 conference in Montreal. The authors are Sylvie Lafortune, Social Sciences Librarian at Carleton University in Ottawa, and Joël Rivard, Geography and GIS Librarian at the University of Ottawa. The article is a commendable example of how to encompass and illuminate an area of research not only though data but also by including the data producers and stakeholders, and the relationships between them. The article is based upon a study conducted in 2017-2018 that explored the data story behind the cycling infrastructure in Ottawa, Canada’s capital city; or to be precise, the infrastructure of the cycling network of over 1,000 km which spans both sides of the Ontario and Quebec provincial boundary known as the Ottawa-Gatineau National Capital Region. The municipalities invest in cycling infrastructure including expanded and improved bike lanes and paths, traffic calming measures, parking facilities, bike-transit integration, bike sharing and training programs to promote cycling and increased cycling safety. The research included many types of data among which were data from telephone interviews concerning ‘who, where, why, when, and how’ in an Origin-Destination survey, data generated by mobile apps tracking fitness activities, collision data, and bike counters placed in the area. The study shows how a narrow subject topic such as cycling infrastructure is embedded in complicated data and many relationships.
 Ningning Nicole Kong is the author of ‘One Store has All? – the Backend Story of Managing Geospatial Information Toward an Easy Discovery’. Many libraries are handling geographical information and my shortened version of the abstract from the article promises: GeoBlacklight and OpenGeoportal are two open-source projects that initiated from academic institutions, which have been adopted by many universities and libraries for geospatial data discovery. The paper provides a summary of geospatial data management strategies by reviewing related projects, and focuses on best management practices when curating geospatial data. The paper starts with a historical introduction to geospatial datasets in academic libraries in the United States and also presents the complicatedness involved in geospatial data. The paper mentions geoportals and related projects in both the United States and Europe with a focus on OpenGeoportal. Nicole Kong is an assistant professor and GIS specialist at Purdue University Libraries. 
 Sophie 1.0 was an attempt to create a multimedia editing, reading, and publishing platform. Based at the University of Southern California with national and international collaboration, Sophie 2.0 was a project to rewrite Sophie 1.0 in the Java programming language. The author Jasmine S. Kirby gives the rationale for the article ‘How NOT to Create a Digital Media Scholarship Platform: The History of the Sophie 2.0 Project’ in the sentence: ‘Understanding what went wrong with Sophie 2.0 can help us understand how to create better digital media scholarship tools’. For the first time we now have failure among the keywords used for a paper in IQ. The Institute of the Future of the Book (IFB) was a central collaborator in the development of the Sophie versions. The IFB describes itself as a think-and-do tank and it is doing many projects. The Kirby paper gives us a brief insight into the future of reading, starting from basic e-books in the 1960s. When you read through the article you will note caveats like lack of focus on usability and changing of the underneath software language. The article ends with good questions for evaluating digital scholarship tools.
 Submissions of papers for the IASSIST Quarterly are always very welcome. We welcome input from IASSIST conferences or other conferences and workshops, from local presentations or papers especially written for the IQ. When you are preparing such a presentation, give a thought to turning your one-time presentation into a lasting contribution. Doing that after the event also gives you the opportunity of improving your work after feedback. We encourage you to login or create an author login to https://www.iassistquarterly.com (our Open Journal System application). We permit authors 'deep links' into the IQ as well as deposition of the paper in your local repository. Chairing a conference session with the purpose of aggregating and integrating papers for a special issue IQ is also much appreciated as the information reaches many more people than the limited number of session participants and will be readily available on the IASSIST Quarterly website at https://www.iassistquarterly.com. Authors are very welcome to take a look at the instructions and layout:
 https://www.iassistquarterly.com/index.php/iassist/about/submissions
 Authors can also contact me directly via e-mail: kbr@sam.sdu.dk. Should you be interested in compiling a special issue for the IQ as guest editor(s) I will also be delighted to hear from you.
 Karsten Boye Rasmussen - February 2019

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.330
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it