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Record W2298580697 · doi:10.5206/tjr.2016.1.4.5

Accessible and Interactive

2016· article· en· W2298580697 on OpenAlex
Tim Rosenkranz, Alexandre Jaillon

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.

venuePublished in a venue whose home country is Canada.
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

VenueTransitional justice review · 2016
Typearticle
Languageen
FieldComputer Science
TopicData Analysis with R
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceVisualizationData scienceData visualizationInformation visualizationGeovisualizationGraphicsData miningInformation retrievalComputer graphics (images)

Abstract

fetched live from OpenAlex

The production and use of datasets is a growing area in transitional justice research. One constant limitation, however, is the way this data is visualized. Relying only on static graphics and tables, many of these datasets are insufficiently explored and analyzed, and remain inaccessible for other researchers. Interactive data visualization tools are an ideal method for overcoming this gap. They are able to adequately present a wide range of quantitative and qualitative data- types, such as geographic, temporal, network, and text data, and their interactive functions allow for a better exploration and understanding of the data. This article examines the visualization needs of transitional justice research, and demonstrates how interactive visualization can facilitate data analysis as well as information sharing. Presenting selected tools for different data types, the article provides hands-on methodological examples for effective handling of transitional justice data using, for example, GIS mapping, Google Motion Charts, and Word Trees.

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: Methods · Consensus signal: none
Teacher disagreement score0.843
Threshold uncertainty score0.320

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.019
GPT teacher head0.302
Teacher spread0.284 · 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