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Record W2036333174 · doi:10.1145/351936.351953

Choosing an object-oriented domain framework

2000· review· en· W2036333174 on OpenAlex
Garry Froehlich, H. James Hoover, Paul Sorenson

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 Computing Surveys · 2000
Typereview
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceCitationDomain (mathematical analysis)Object (grammar)Library scienceArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Deciding whether or not a framework 'fits' an application and provides an appropriate basis for development of the application is one of the key decisions developers make when choosing to use a new framework. In this paper we outline a three step process for helping to determine whether or not a framework is appropriate. The process looks at the limitations, the hooks and the amount of uncertainty in using a framework. 1 Introduction Object-oriented frameworks [2] enable developers to rapidly produce new applications --- provided that the framework is actually suited to the requirements of the new application. Often, previous experience with the framework is used to make this decision, but when application developers are unfamiliar with a new framework they have no experience for deciding whether or not to use it. They may not discover that a framework lacks support for key requirements of an application until well into the development cycle, resulting in substantial redevelopment or ...

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)0.007
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0070.002
Research integrity0.0010.002
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 head0.053
GPT teacher head0.358
Teacher spread0.306 · 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