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Record W2245256784 · doi:10.1145/2830719.2830735

Report on the First International i* Teaching Workshop (iStarT)

2015· article· en· W2245256784 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

VenueACM SIGSOFT Software Engineering Notes · 2015
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVariety (cybernetics)Computer scienceEngineering managementEngineering ethicsSoftware engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The i* Framework, facilitating goal-oriented information systems modeling, has received much attention in research since its introduction. As the i* and related frameworks (e.g., GRL and Tropos) have been in existence for more than 20 years, researchers around the world have accumulated experience in teaching such languages, at both the undergraduate and graduate levels, to students with a wide variety of backgrounds. It is our aim to begin to collect and share these experiences. As such, we organized the first International iStar Teaching Workshop (iStarT'15), a focused workshop covering topics related to i* and goal-oriented pedagogy held at the Conference on Advanced Information Systems Engineering (CAiSE'15). In this report we summarize the presentations, discussion and future plans made as part of iStarT'15.

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)0.246
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.715
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.246
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.040
GPT teacher head0.278
Teacher spread0.238 · 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