Bibliographic record
Abstract
Summary form only given. For the computing discipline, history seldom receives status. From a cultural standpoint history broadens one's perspective on the field and lets students and scholars explore the inner thinking of people and the events they produced. From a practical standpoint, history enables individuals and enterprises to learn from the events of the past and to improve on experiences. Both views are necessary to create an informed computing professional. Teachers often ignore history of computing when teaching their computing courses. The typical case is that educators mention some facts or important milestones usually related to hardware and the people responsible for these. The discussion then moves quickly to other topics. This is unfortunate, because we can learn much from history. History is the best teacher to assess the meaningful evolution of the computing profession. The IFIP Joint (TC3-TC9) Task Group has examined this concern and is producing a report entitled: History in the Computing Curriculum. The report addresses the need to include history in the curriculum and serves as a basis for this proposal. It also provides educators, whose formal study of computing history is minimal or nonexistent, ways in which to teach computing history in an educational environment. This paper presents the work of the IFIP Joint Task Group to a computing and engineering audience.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.304 | 0.120 |
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".