Future visioning for sustainable household practices: spaces for sustainability learning?
Bibliographic record
Abstract
Despite widely articulated concerns about unsustainable production and consumption processes, governance interventions have led to only incremental shifts in routinised production and consumption behaviour, particularly within households of western, industrialised societies. In response, techniques of future visioning have been mooted as more ambitious governing mechanisms that could help to liberate policymakers and other stakeholders from current patterns of disjointed incrementalism in the field of sustainable production and consumption. At the heart of these claims is the assertion that visioning promotes learning that can lead to the emergence of innovative approaches to sustainability challenges from problem redefinition to practical action. This paper examines the extent to which participatory visioning creates spaces for sustainable learning using empirical evidence from workshops focused on transforming household consumption practices in Ireland. It is concluded that participatory visioning approaches do provide supportive physical places and intellectual spaces for personal and collaborative learning with regard to potential sustainability transformations. The bounded nature of the particular workshops examined, in terms of duration, focus and participants, means that embedding such learning within wider organisational structures and practices is likely to be a much less certain process that, if it does occur, will unfold over longer timescales and in unpredictable ways.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".