Bibliographic record
Abstract
Interviews with stakeholders can be a useful method for identifying user needs and establishing requirements. However, interviews are also problematic. They are time consuming and may result in insufficient, irrelevant or invalid data. Our goal is to re-examine the methodology of interview design, to determine how various contextual factors affect the success of interviews in requirements engineering. We present a case study of a Web conferencing system used by a support group for spousal caregivers of people with dementia. Two sets of interviews were conducted to identify requirements for a new version of the system. Both sets of interviews had the same information elicitation goals, but each used different interview tactics. A comparison of the participants' responses to each format offers insights into the relationship between the interview context and the relative success of each interview technique for eliciting the desired information. As a result of what we learned, we propose a framework to help analysts design interviews and chose tactics based on the context of the elicitation process. We call this the contextual risk analysis framework.
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 | 0.132 | 0.206 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".