Social enterprise as poverty reducing strategy for women
Bibliographic record
Abstract
Purpose – The purpose of this study is to investigate the potential of social enterprise as a strategy for poverty reduction for women. Design/methodology/approach – A literature synthesis on the topic was conducted and patterns, linkages and gaps were examined among key themes to identify how social enterprise can potentially serve as a poverty reduction strategy for women. Findings – The paper presents the findings in terms of specific factors contributing to women’s poverty and hypothesizes mechanisms through which social enterprises can mitigate or address these factors in practice. The paper organizes these findings in an integrative framework that highlights the need to ensure a solid policy foundation is in place before a number of key support mechanisms are enabled, which then facilitate specific types of work that can then grow in a sustainable manner. Research limitations/implications – While the mechanisms and proposed framework are based on the extant literature, additional empirical investigation is required. Practical implications – Women are disproportionately burdened by poverty and the framework presented provides a very practical tool to guide the design of new or diagnosing existing social enterprises targeting poverty reduction for women. Social implications – Without a strategic approach, the risk is either perpetuating the status quo, or worse, placing those women engaged in social enterprises in a worse financial and social position. Originality/value – There is limited research on the poverty reducing role of social enterprise for women and the proposed mechanisms and integrative framework presented provide a means of synthesizing our current knowledge while providing the basis for future investigations.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".