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Record W2300049803 · doi:10.14288/1.0055583

The women-in-development efficiency approach : a case study of programming income generation in a Chinese village

2009· article· en· W2300049803 on OpenAlexaffabout
Diane O. Tyler

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRural developmentEconomic growthBusinessEconomicsGeographyAgriculture

Abstract

fetched live from OpenAlex

In the 1970s, international development planners began to recognize women's important roles in their communities. A variety of approaches to include women have since evolved, and their merits are debated. They have been described in the literature as "welfare," "Women-in-Development" (WID), and "Gender and Development" (GAD). The welfare approach focuses on basic needs while strengthening women's homemaker and reproductive roles. The WID approach is based on increasing women's incomes as a means toward empowerment. The Gender and Development (GAD) addresses systemic gender discrimination. There is need for research in development planning. Development programs track results during the project, but seldom look at long term impacts and sustainability. This thesis reports the results of research on a 1991 WID efficiency approach, women's income generation project in Shaanxi Province, China, by examining the impact seven years later. My methodology involved interviews with twenty-one women project participants, eight husbands, village leaders and informal lunch-hour focus group discussions with villagers. The project involved transition from grain to orchards. The orchards dramatically increased women's incomes and improved the quality of village life. The women took full control of orchard management, pushing men out of the orchards saying that they were "incapable" of the monotonous orchard tasks. Most husbands found off-farm jobs, diversifying household incomes. Women gained marketing skills, self-confidence, and financial independence, but remained vulnerable as primary producers to income fluctuations. Most women stayed outside village politics, and traditional gender role socialization was maintained. The project fulfilled women's needs and interests, however, long term results for women are mixed. The Shaanxi field project was one of sixty-six field projects under the Canada- China Women-in-Development Project (1990-1995) implemented in partnership by the Canadian International Development Agency (CIDA) and the All-China Women's Federation. The project had two components: poverty reduction and institutional strengthening of the Women's Federation. I was the Canadian co-manager of the Canada-China WID Project, and have since completed more than thirty contracts (fifteen projects) plus a two-year contract as co-manager of the Canada-China Women's Law Project (one year of which was full-time in China). My research is intended to assist and improve my future work in the development field, and to inform those interested in women's development program planning and gender equality policy. Good planning was key to the strength of the Canada-China WID Project. Partners shared a common goal. CIDA's efficiency approach supported the Women's Federation policy to bring women into production as a means of achieving equality. Participatory planning and decision-making involved Federation project officers across China. Delegation in management and clear, commonly set guidelines increased partners' involvement and accountability. Power in planning gradually, and tacitly, transferred to the Women's Federation as they assumed ownership and responsibility for results. Strong donor/recipient partnership and participatory planning processes strengthen potential for sustainable results. Suggestions to improve women's development planning include: increasing gender awareness, strengthening women's interest and capacity in political participation, developing risk mitigation strategies to lessen income insecurity, blending WID/GAD projects, and further research on project impacts.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.004
Scholarly communication0.0020.001
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.012
GPT teacher head0.216
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes2
Has abstractyes

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