Bibliographic record
Abstract
An ongoing academic debate shows that urban community gardening (CG) has diverse governance models with differing roles of city administration and citizens. This article uses an empirical case study conducted in the city of Tampere, Finland, to explore what I call the “operational space” of urban CG seen from the viewpoint of city officials. Two rounds of interviews were conducted with eight city officials, and a discourse analysis was applied for the data. As an analytic term developed in this article, the operational space emerges by administrative policies and practices that enable or constrain urban gardening under two general trends of urban governance: institutional ambiguity and neoliberal urban development. In this case, the operational space was rather rigid and narrow. The five main discourses on benefit, control of space, scarcity, unclarity, and newness referred to a clear aim to enable urban gardening. However, the discourses were restricted to strategic, limited, and instrumental levels, as the political-strategic aims of enabling urban gardening contradicted the administrative practices. The results show that cautiousness and unclarity in the administrative-political culture tend to lead to institutional ambiguity. In conclusion, operational space analysis is helpful to uncover the problems and possibilities between CG and city administration.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.055 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".