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Record W2346298098 · doi:10.1145/2851581.2856508

Attending to Objects as Outcomes of Design Research

2016· article· en· W2346298098 on OpenAlexaff
Tom Jenkins, Kristina Andersen, William Gaver, William Odom, James Pierce, Anna Vallgårda

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsSimon Fraser University
FundersEngineering and Physical Sciences Research Council
KeywordsPremiseFraming (construction)ConversationComputer scienceResearch designWork (physics)Knowledge managementConceptual designSpace (punctuation)Interaction designEngineering ethicsManagement scienceHuman–computer interactionPsychologySociologyEngineeringEpistemology

Abstract

fetched live from OpenAlex

The goal for this workshop is to provide a venue at CHI for research through design practitioners to materially share their work with each other. Conversation will largely be centered upon a discussion of objects produced through a research through design process. Bringing together researchers as well as their physical work is a means of gaining insight into the practices and outcomes of research through design. If research through design is to continue to develop as a research practice for generating knowledge within HCI, this requires developing ways of attending to its made, material outcomes. The premise of this workshop is simple: We need additional spaces for interacting with and reflecting upon material design outcomes at CHI. The goal of this workshop is to experiment with such a space, and to initially do so without a strong theoretical or conceptual framing.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.169
metaresearch head score (Gemma)0.202
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.169
Threshold uncertainty score0.895

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1690.202
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.005
Science and technology studies0.0080.036
Scholarly communication0.0380.028
Open science0.0050.029
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0080.002

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 head0.160
GPT teacher head0.424
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations14
Published2016
Admission routes1
Has abstractyes

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