Towards a Better Understanding of the Different Computing Disciplines
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.033 | 0.067 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.011 | 0.029 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".