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Record W2808654760

DataTours: A Data Narratives Framework

2017· preprint· en· W2808654760 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

VenueLillOA (Université de Lille (University Of Lille)) · 2017
Typepreprint
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsNarrativeComputer scienceData scienceArtLiterature
DOInot available

Abstract

fetched live from OpenAlex

Visual storytelling is commonly employed to communicate data analyses results.Alternatively, (semi-)automated [1,2,6] data narratives or "tours" have been proposed as a means to prompt exploration of massive multidimensional datasets, substituting the more prevalent static overviews.While these works demonstrate specific instances of data tours, a concrete model to describe the building blocks of such tours is lacking.We present a descriptive hierarchical framework, DataTours, to formalize and guide the design of (semi-) automated tours for data exploration and discuss challenges evoked by the framework in the (semi-)automated authoring of such tours.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Open science
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.431
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0150.031
Research integrity0.0010.001
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 head0.053
GPT teacher head0.285
Teacher spread0.232 · 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