Het deblokkeren van de sociaal-politieke dynamiek van alternatieve voedselnetwerken door middel van een hybride Bestuursaanpak. Hoogtepunten uit het Brussels-Hoofdstedelijke Gewest en Toronto
Bibliographic record
Abstract
This dissertation focuses on the governance of alternative food networks (AFNs). The aim is to identify, conceptualize and empirically investigate the critical governance tensions conditioning the genesis and the life-course of alternative food initiatives. To this purpose this dissertation develops a Hybrid Governance Approach (HGA) which identifies three types of governance tensions - i.e. organizational, resource and institutional - and analyses the interrelations among them in different case-studies of local food initiatives in the Brussels-Capital Region. An international case study - Toronto - is investigated to learn from similarities and differences in the ways local food networks experience and address governance tensions in the two city-regions' food policy trajectories. The empirical findings of this dissertation help to unravel the contradictions and dilemmas that AFNs face in their dynamic reproduction. The need to cope with their own spatial-material growth, to secure necessary material-operational resources - among which arable land to feed (alternative) food systems - as well as the necessity to deal with often contradictory multi-level socio-institutional environments are among the key factors of governance tension in AFNs. The analysis is also attentive to the outcomes of the governance tensions in the life-course of local food initiatives and thus to the promising organizational strategies, self-reflexive and co-learning dynamics put into place by AFNs to cope with the experienced tensions or to channel them into sustainable directions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".