Bibliographic record
Abstract
Management research has recently begun to shed new light on the role and nature of business innovations targeted at subsistence marketplaces (Viswanathan and Rosa 2007), the four billion poor that have also been referred to as constituting the Bottom of the Pyramid (Prahalad 2005). The notion that ways might be found for business to effectively serve the needs of subsistence markets is gaining increasing currency, and holds promise for both firms and consumers. For firms, it constitutes potential access to a vast, undertapped market for products and services. For subsistence consumers, it includes the promise of affordable access to products hitherto unaffordable or unavailable. Although gaining momentum, this viewpoint still faces many challenges, including the central question of whether business really can help to overcome the problem of poverty. We contend that the best way to begin to address such issues is to develop deep understanding of the lives of individuals living in subsistence conditions. This paves the way for a bottom-up, grounded understanding of the potential for business to contribute to economic and social development among the poor. Our subsistence marketplaces perspective is a bottom up approach to understanding buyer, seller, and marketplace behavior that complements mid-level business strategy approaches, such as the base of the pyramid approach, and macro-level economic approaches to studying business and poverty that currently exist in the literature. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".