Location and Logic of Networks for Entrepreneurs in the Socially Responsible Consumption Sector in Québec
Bibliographic record
Abstract
Many studies in social sciences have focused on socially responsible consumers (Dubuisson-Quellier 2004; Lamine 2003; Poncelet 2005; Queniart, Jacques, and Jauzion 2007a and 2007b) but only a few have looked at entrepreneurs. Recent studies in this area have mostly been interested in the “economization of the political and social scenes” as well as in social economic movements (Salmon 2002; Johnson 2003; Gendron et al. 2006). For our part, we have decided to look into Quebec entrepreneurs who offer responsible products (fair trade, organic, local products). We want to analyze the relationship between their economic mission (for profit) and their social mission (centered on the promotion of ethical values and practices, solidarity or respect for the environment). In this paper, we focus on the relationships between these entrepreneurs and their competitors, particularly on the importance they assign to the networks they nurture in and around their enterprise. Based on an analysis of forty interviews conducted in Quebec with male and female entrepreneurs from different regions, we show that they build specific networks not commonly found in the conventional sector. We have categorized them into four distinct types: business networks, business and values networks, solidarity networks, and “shop-networks,” each with its own logic.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".