On the Implications of Human-Paper Interaction for Software Requirements Engineering Education
Bibliographic record
Abstract
It is broadly accepted that requirements engineering is one of the most important phases of a software project, and requires tools to be effective.For a variety of reasons, paper as a tool has lasted for millennia and remains ubiquitous.This paper makes a case for a contextual, conscientious, and evidence-based use of paper in a competency-oriented approach to software requirements engineering education (REE).It argues that the prophecies for the obsolescence of paper are premature, there are unique benefits in the use of paper, and the decision to use paper should be based on [0, 1] rather than {0, 1}.In this regard, a need-centered conceptual model for human-paper interaction is proposed.The characteristics of paper that make it historically unique are reported and the affordances of paper relevant to REE are discussed.The REE-related activities that benefit from viewing paper as a boundary object and using different types of paper are highlighted and illustrated by means of examples.In advocating polyliteracy, the potential for a convergence of paper and digital media towards a harmonic coexistence is underscored.
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.
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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".