Innovating through commons use: community-based enterprises
Bibliographic record
Abstract
Community-based enterprises are of interest to commons researchers because they offer a means to study how local institutions respond to opportunities, develop networks, new skills and knowledge, and evolve. Nevertheless, the relationship between commons and community-based enterprises has received little attention, with a few exceptions (Bray et al. 2005; Berkes and Davidson-Hunt 2007). Therefore, we decided to organize a conference session and explore this relationship in more detail. We invited a diverse array of scholars and practitioners active with indigenous enterprises, community development, community forestry, ecotourism and conservation-development projects. This Special Issue includes peer-reviewed and edited versions of seven of the papers (plus two additional invited papers) presented at the two panels on “Innovating through commons use: community-based enterprises”, at the 12th Biennial Conference of the International Association for the Study of the Commons (IASC 2008) in Cheltenham, England.
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 | 0.006 | 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 | 0.007 | 0.015 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".