MétaCan
Menu
Back to cohort
Record W2179780355 · doi:10.1145/2817721.2817735

Understanding Researchers' Use of a Large, High-Resolution Display Across Disciplines

2015· article· en· W2179780355 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsZoomMultitudeContext (archaeology)Data scienceVariety (cybernetics)Computer scienceResolution (logic)Human–computer interactionEngineeringArtificial intelligenceEpistemologyGeography

Abstract

fetched live from OpenAlex

A driving force behind the design of increasingly large and high resolution displays (LHRDs) has been the need to support the explosion of data in the natural sciences such as physics, chemistry, and biology. However, our experience with an LHRD accessible to researchers across multiple disciplines has shown that they are useful for a wide range of research activities involving large images and data. \ We conducted in-context, semi-structured interviews with researchers from a variety of disciplines about their experiences using the LHRD with their own data. Notably, it became apparent that the size and resolution of the LHRD supported a multitude of activities related to observation, for which zooming or other enlargement methods on standard resolution screens were not sufficient. The interview findings lead to implications for further research into supporting a broader range of disciplines in using large, high-resolution displays.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.978
Threshold uncertainty score0.225

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.412
GPT teacher head0.439
Teacher spread0.028 · 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

Quick stats

Citations25
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicData Visualization and AnalyticsFrench-language works237,207