Design With a Positive Lens: An Affirmative Approach to Designing Information and Organizations
Bibliographic record
Abstract
Design forms one critical paradigmatic view that pervades organizational studies, management, and information systems research. Building on the discussions in the first Working Conference on Designing Information and Organizations with a Positive Lens, we chart the potential contribution of positive design to the shaping of organizations, work processes, artifacts, communication networks, and information technologies. The figure of speech "Design with a Positive Lens," or in short "Positive Design," connotes here a distinctive perspective on design that is less focused on the detection of errors associated with gaining control and more concerned with human-centered design associated with the shaping of hopeful organizations and a thriving future. The paper examines how positive design can contribute to the design of information systems and organizations as related to five broad-scale areas: design of high performance work processes; positive design methods and techniques; cooperation and collaboration across boundaries to promote positive change; positive organizational design; and design science and practice. In this paper we aspire to promote the emerging cross-disciplinary discourse between scholars and designers that will foster positive organizational and technological design.
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 | 0.065 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.078 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".