Designing Marketplace Literacy Education in Resource-Constrained Contexts: Implications for Public Policy and Marketing
Bibliographic record
Abstract
This article describes the findings of an immersive program of field research on consumers living in poverty in South India and the lessons learned from the development and operation of educational interventions designed to enhance the marketplace literacy of these consumers. Whereas extant research and practice have traditionally addressed two key factors that facilitate market participation for the poor—market access and financial resources—the current research focuses on a third critical and complementary factor—namely, marketplace literacy. The authors contend that to sustainably benefit from enhanced market access and resources, (1) people living in subsistence conditions need to develop tactical or procedural knowledge, or concrete “know-how,” regarding how to be an informed consumer or seller, and (2) this know-how must be grounded in conceptual/strategic knowledge, or “know-why” understanding, of marketplace exchanges. To that end, the educational program outlined begins by familiarizing participants with the purpose and logic of marketplaces and then transitions to the tangible aspects of how these marketplaces function. The article concludes with reflection on the implications for consumer policy, marketing research, and business practice.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".