Social Enterprise as a Broker of Identity Resources
Bibliographic record
Abstract
Social enterprises often transmit pro-social values to their staff, volunteers, stakeholders, and communities. Research also shows that social enterprises can improve aspects of beneficiaries’ identity and self-worth. However, knowledge about identity-construction dynamics among social enterprises, their founders and other stakeholders, and the communities and cultures in which they are situated is undertheorized and fragmented across fields. This is attributable, at least in part, to the lack of a theory that can explain identity construction across micro-individual, meso-organizational, and macro-cultural levels. This study makes two major contributions. First, we advance a novel, multi-level theoretical framework for understanding identity construction based on assemblage theory. Second, we use that framework to interpret data from our ethnographic study of a social enterprise based in a Canadian fishing village. Our study reveals that the social enterprise actively curates identity resources from local culture and heritage and brokers those resources to stakeholders for their personal identity projects. It suggests that the impacts are greater for people with transitional or problematic identities. It also shows that identity-resource brokerage can result in generativity whereby staff and volunteers “pay it forward” with the effect of scaling the social impact of the enterprise. The findings support the usefulness of the identity-as-assemblage construct for understanding complex identity dynamics across multiple levels of analysis. They also open the door to a number of provocative research questions, including the role of narrative transmission in the flow of identity resources and a potential identity-mirroring role for social enterprise in shaping or reinforcing elements of place identity.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".