Bibliographic record
Abstract
As experiments and models of participatory, sustainable living, ecovillages demonstrate how to enact just, cooperative, and regenerative economic and social constructs, as alternatives to ‘unsustainable’ capitalist economies and consumerist/individualistic lifestyles. Work is central to these enactments, which provides an opportunity to examine the learning that happens in these spaces, and how that learning may be applied for broader eco-social change. This paper reports on case studies of learning through enterprise work in two ecovillages in the USA. Analysis focuses on what is learned and how it is learned, the role of the learning environment and interactions within the ecovillage on learning outcomes and processes, as well as barriers to learning, and the transferability of learning outside the ecovillage context. Findings evidence a high degree of informal ‘on the job’ learning, resulting in both job-specific skills and knowledge, and general competencies in eco/ethical business management. Furthermore, participants imbue activities with shared values of ecology and equality, while interacting with oppositional broader market logics, and thus learn to ‘trade off’ – taking on some aspects of the mainstream economy (e.g. competitiveness, profitability, (self)exploitation), in exchange for ‘the greater good.’
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".