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Record W1741715216 · doi:10.1002/0470013192.bsa261

Graphical Presentation of Longitudinal Data

2005· other· en· W1741715216 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

VenueEncyclopedia of Statistics in Behavioral Science · 2005
Typeother
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceTRACE (psycholinguistics)GraphicsPresentation (obstetrics)Representation (politics)Simple (philosophy)Process (computing)Longitudinal dataTheoretical computer scienceExternal Data RepresentationData scienceComputer graphics (images)Artificial intelligenceProgramming languageData miningLinguisticsEpistemology

Abstract

fetched live from OpenAlex

Abstract There are some ideas that are so ubiquitous that we cannot imagine the world without them: language and numbers come to mind as immediate examples. Data‐based graphics provide a third example. The graphical representation and communication of data is such a simple and clear process that it is no easier to think of a world before graphs than a world before arithmetic. Yet, graphs were invented and, moreover, invented relatively recently. The very first plots were graphs of longitudinal data. In this essay, we will trace some of the development of graphs for the display of longitudinal data.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.403
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0030.001
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 head0.052
GPT teacher head0.388
Teacher spread0.335 · 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