Bibliographic record
Abstract
Context: Software increasingly shapes daily life, making requirements engineering (RE) essential for ensuring systems contribute to community social sustainability. Yet automated elicitation practices risk distancing RE from the cultural, social, and political contexts that inform user needs, systematically excluding the communities most dependent on socially impactful software. AI-assisted RE has intensified this trend. Objective: This paper introduces REConnect, a human-centered participatory RE framework that recenters requirements work on human connection and relationality as the foundation for understanding lived experiences and ensuring alignment with community values and aspirations. Methods: REConnect was derived through qualitative analysis of 26 community-engaged software projects conducted through the INSPIRE program at the University of Victoria between 2022 and 2025, spanning rural Nepal, urban Canada, and remote Arctic Canada. We conducted a reflective synthesis across all 26 projects, followed by in-depth thematic analysis of three illustrative projects. Results: Three core principles are articulated: building trusting relationships, co-creating with and alongside stakeholders, and empowering users as agents of change. Each is operationalized through actionable REConnect Actions (REActions) embedding relationality and continuous stakeholder engagement throughout the project lifecycle. Conclusions: REConnect positions human connection as the foundation of RE for socio-technical systems aiming toward social sustainability. While AI can accelerate certain RE activities, its integration must be governed by participatory principles that preserve human agency and ensure marginalized voices are not excluded. We discuss how REConnect integrates with AI support while maintaining critical human agency in requirements engineering.
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 | 0.046 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".