Community Involvement In The Hybrid Organization: A Study Of Community Forest Enterprises in Canada
Bibliographic record
Abstract
Effectively involving local communities in decision-making is an important facet of all organizations which aim to be socially responsible, but it is particularly relevant for social enterprises (SEs) which espouse community empowerment as one of their central goals. The extant literature, however, provides little in the way of guidance for SEs to ensure local participation in strategic and operational matters. This qualitative study illustrates mechanisms by which SEs can involve local people in organizational decisions in the context of six Canadian community forest enterprises (CFEs). CFEs have evolved as a result of the movement to decentralize decision-making with respect to the sustainable utilization of natural resources. This article provides insights on the importance of managerial capabilities in navigating participatory activities in the SE. We propose an enriched SE tension management framework, that includes a key dimension that has yet to receive much attention: the distinction between a focus on ‘outcomes’ versus a focus on ‘process’ in the attainment of plural goals. As such, the article makes important contributions to unpacking the empowerment element underlying organizational activities that aim to provide marginalized rural and Indigenous communities with roles in shaping local paths towards greater sustainability.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.033 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".