Socio-cultural web and environmentally-driven community entrepreneurship: a portrayal of Abia Ohafia community in South-Eastern Nigeria
Bibliographic record
Abstract
Purpose This study is aimed at providing a deduction on the necessity of social and cultural capital for entrepreneurial outcomes on a community-wide scale. Design/methodology/approach There is a drift from an individualised form of entrepreneurship to community-based entrepreneurship with a grand focus on social needs of current and emergent nature. This study is both archival and exploratory and has pictured culture and communality as drivers that are needful for enterprising communities. Findings This paper finds communality, social network, social capital and trust as push-factors for community-based entrepreneurship and development drives. Originality/value This study is an original exposé on the Abia Ohafia community’s Model of community-based entrepreneurship which thrives on strong institutions (like the Age Grade System) and age-long practices that have built trust and stability. This local community through its networks, culture and communalities creates relationships, rational innovation, consensual leadership and participatory followership under which resources, opportunities and solutions are deliberately advanced for meeting social and community purposes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".