Bibliographic record
Abstract
This paper provides a brief overview of a number of experimental interface design projects being carried out collaboratively by teams of researchers at the University of Alberta and elsewhere. One goal of this interface research is to explore the principles of rich-prospect browsing interfaces, which I have defined (Author 2003) as those where some meaningful representation of every item in a collection is combined with tools for manipulating the display. Often this manipulation is for the purposes of carrying out some portion of a research task: the interfaces lend themselves to exploratory and synthetic activities, such as knowledge discovery and hypothesis formulation. The projects summarized here begin with a browsing prototype originally designed for the task of pill identification (Given et al. 2005) but subsequently extended into a prototype for browsing conference delegates and other groups of people (Author et al. 2006). Another is a nuanced system based on the mandala (Cheypesh et al. 2006) intended for examining any collection that has been encoded with an XML schema, using combinations of attractors selected by the user from the available tags. Next is the set of specialized interfaces for the Orlando Project (Orlando Team 2006), intended to provide a set of discrete entry points into the deeply-encoded electronic history of women’s writing in the British Isles. Our project on tabular interfaces provides a variety of spaces designed to assist the user in using thesauri for multilingual query enhancement (Anvik et al. 2006). The final project described below is NORA (Unsworth 2004), which relies on the power of the D2K data-mining tools at the National Centre for Supercomputing Applications at the University of Illinois at Urbana-Champaign to give humanities scholars a workspace for exploring the system-identified features of common documents and further documents that have been recommended by the system. Each of these projects are discussed within the framework of visualizations involving browsing through dynamic grouping.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.032 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".