The design process : designing information technology for the public sphere
Bibliographic record
Abstract
The public sphere represents dynamic spaces in our cities and communities that attract mass users and that promote information sharing in multifaceted yet mutually beneficial ways, often through the use of information technology (IT). Gauging the effectiveness of current IT for the public realm raises key questions on how designers interpret, approach and create information technologies for public spaces. In this thesis, I explored different aspects of the design process in the public realm, specifically The Museum of Anthropology (MOA) in Vancouver, Canada. I investigated such questions as: What are the design goals of creating such a system? Who is involved? What are the challenges and opportunities? In order to better understand the thinking, practices and vision of people involved in the design process, this thesis conducted an in-depth case study of the Museum of Anthropology Collections Access Terminal and Digital Catalogue System (MOA CAT). The MOA CAT system is a new, interactive information kiosk and public access system designed for museum visitors to search, retrieve and explore the museum’s collection. The methodology used in this thesis was a case study in which I interviewed participants involved in the design of the MOA CAT and reviewed documentation that spanned a decade of planning, building, and implementing the technology at the MOA. The purpose of this research was to understand the design process through the lens of the interdisciplinary team consisting of Information Manager, Designer, Project Manager, Communication Manager, Exhibit Designer and Museum Collection Manager. The findings emphasize that the design goals of the MOA CAT were to engage users, encourage exploration of information, and provide resources through an accessible information system. The design process observed in this case study of MOA demonstrated that design in the public and organizational spheres is an ongoing and fluid process driven by group collaboration and the formation and execution of key design goals, goals that ultimately encouraged meaningful interactions and exploration of a public space.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.076 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".