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Record W2979404523 · doi:10.1145/3359591.3359738

Learning to listen for design

2019· article· en· W2979404523 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

In his essay, Designed as Designer, Richard Gabriel suggests that artifacts are agents of their own design. Building on Gabriel’s position, this essay makes three observations (1) Code “speaks” to the programmer through code smells, and it talks about the shape it wants to take by signalling design principle violations. By “listening” to code, even a novice programmer can let the code itself signal its own emergent natural structure. (2) Seasoned programmers listen for code smells, but they hear in the language of design principles (3) Design patterns are emergent structures that naturally arise from designers listening to what the code is signaling and then responding to these signals through refactoring transformations. Rather than seeing design patterns as an educational destination, we see them as a vehicle for teaching the skill of listening. By showing novices the stories of listening to code and unfolding design patterns (starting from code smells, through refactorings, to arrive at principled structure), we can open up the possibility of listening for emergent 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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.234
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.061
GPT teacher head0.308
Teacher spread0.247 · 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