Building the Social Structure of a Market
Bibliographic record
Abstract
Motivated by the question of how to develop viable new markets and value chains in the resource-constrained settings of least developed countries, we adopted multi-year qualitative methods to examine the intervention of a nongovernmental organization (NGO) in developing the dairy value chain in Bangladesh. Consistent with the theoretical premise that markets and value chains are social orders, we found that the NGO’s success relied on building the social structure of a market wherein market participants could negotiate relationships and norms of production and exchange and embed them in practices and technologies. To establish social structure among participants as a means of market building, the NGO acquired relevant knowledge, then used contextual bridging (transferring new meanings, practices and structures into a given context in a way that is sensitive to the norms, practices, knowledge and relationships that exist in that context), brokering relationships along the value chain (facilitating introductions and exchanges between value chain members) and funding experimentation (providing resources to test ideas and assumptions about new market practices). Market participants themselves also contributed to the development of the market’s social structure by means of social embedding (building relationships and negotiating norms of exchange and coordination), and material embedding (implementing technologies and practices and integrating market norms into technology). Increased productivity and equity and reduced costs of transactions resulted from the creation of a social structure that, in this case, preceded and enabled the economic structuring of a market rather than the other way around.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.036 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".