Bibliographic record
Abstract
While Inclusive Design and Frugal Innovation have gained separate academic interest in recent years, there lacks research on their integration. This Master’s major research project asks How might social enterprises in Ontario integrate Inclusive Design with Frugal Innovation to maximize economic and social value? The following theoretical framework is put forth: a harmony of Inclusive Design and Frugal Innovation would add value to the social enterprise model by using minimal resources to design for the maximum amount of people. This paper begins with an introduction to the subject matter by outlining key concepts and situating them within the research context. A literature review is then put forth to examine the research about Inclusive Design, Frugal Innovation, and social enterprise to provide a rationale for the theoretical framework. The Methodology chapter explains how using qualitative interviews and General Morphological Analysis as a foresight tool explore how these concepts could exist in a symbiotic relationship to make Frugal Inclusive Design. The Findings & Discussion chapter explores the opportunities and barriers for social enterprises to adopt this new concept as an integral part of their business. The research shows that social enterprises have adopted Inclusive Design and Frugal Innovation principles with varying degrees of success. It is suggested that social enterprises use lead user theory to strengthen the relationship between Inclusive Design and Frugal Innovation. This paper ends with a conclusion and suggestions for areas of future research.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".