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Record W4396832691 · doi:10.1145/3613905.3636318

Human-Notebook Interactions: The CHI of Computational Notebooks

2024· article· en· W4396832691 on OpenAlexaff
Jesse Harden, April Yi Wang, Rebecca Faust, Katherine E. Isaacs, Nurit Kirshenbaum, John Wenskovitch, Jian Zhao, Chris North

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceIntersection (aeronautics)Computational thinkingOpen researchComputational modelMetaphorHuman–computer interactionInterface (matter)Data scienceArtificial intelligenceWorld Wide WebEngineering

Abstract

fetched live from OpenAlex

The overall goal of this workshop is to bring together researchers from across the CHI community to share their knowledge and build collaborations at the intersection of computational notebook and HCI research, focusing on both the effective design and effective use of interfaces and interactions within computational notebook environments. This includes innovating upon the computational notebook metaphor, designing new tools, interfaces, and interactions for use with computational notebooks, and more. We aim to pull expertise from across all fields of CHI to deliver novel research and generate open discussion about the current state of computational notebooks, how it can be improved from an HCI standpoint, and how these potential improvements can direct future research. To achieve this goal, we propose a full-day, hybrid workshop with discussions of challenges and opportunities, paper and demo presentations, lightning talks, and a keynote. Participants in this workshop will exchange ideas and help define a roadmap for future research at the intersection of HCI and computational notebook design.

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 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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.192

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 communication0.0000.000
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 head0.038
GPT teacher head0.356
Teacher spread0.318 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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