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Record W2032558138 · doi:10.1145/2808006.2808016

Towards a Better Understanding of the Different Computing Disciplines

2015· article· en· W2032558138 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
TopicInformation Systems Education and Curriculum Development
Canadian institutionsMount Royal University
Fundersnot available
KeywordsField (mathematics)AmbiguityComputer scienceEnd-user computingData scienceInformation technologySoftwareDisciplineUtility computingCloud computingSociology

Abstract

fetched live from OpenAlex

The field of computing has undergone significant differentiation over the past twenty years, resulting in several distinct computing sub-disciplines. After extensive consultation with experts and industry stakeholders, the ACM [1] defined five distinct sub-disciplines within the computing field: computer science (CS), information systems (IS), computer engineering (CE), software engineering (SE), and Information technology (IT). While these areas are unique, they are not completely discrete, and there seems to be ambiguity around which tasks fit into which sub-discipline. The ACM has made significant efforts to define these in terms of expected program content and by the outcomes and skills required to prepare students for the dynamic labor market. Nonetheless, research [4,5,6,9] shows that there is a need for an even clearer understanding of these sub-disciplines by the academic community, by guidance and career counsellors, and by, of course, prospective students.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.119

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 head0.078
GPT teacher head0.285
Teacher spread0.207 · 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