Navigating Co-Production: A Complexity-Informed Volunteer Ethnography of Social Enterprise Policy in Nova Scotia
Bibliographic record
Abstract
What happens when stakeholders from across sectors team up to design and implement public policy? Spoiler alert: It can be messy, innovative, dramatic and inspiring. This thesis explores the experiences and perspectives of key stakeholders, including my own, as we contributed to the co-production of public policy related to the social enterprise sector in Nova Scotia, Canada. After characterizing the co-production process as a complex adaptive system, this qualitative research employed volunteer ethnography across three organizations with 19 semi structured interviews. Participants included key stakeholders from multiple sectors who played a role in co-producing the Advancing Social Enterprise in Nova Scotia strategy. Findings indicate that co-production is heavily influenced by territorial dynamics, institutional and individual path dependencies, and the inherent role fluidity of co-producers as they juggle the “multiple hats” they wear, at times balancing individual and organizational interests with collective policy objectives. The findings highlight territoriality as a critical factor in the co-production process, underscoring the importance of recognizing and mitigating its effects to ensure effective collaboration. This research makes three key contributions: (1) it provides the first empirical integration of co-production, territoriality, and complex adaptive systems (CAS); (2) it offers new theoretical insights into the emergent, non-linear nature of co-production processes; specifically territoriality; and (3) it presents practical policy recommendations to enhance stakeholder collaboration in co-production initiatives: specifically the value of a CAS lens to shape and monitor the co-production engagement from design through closing phases.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".