Stories from Globalgiving Storytelling Project

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2 stories matched (16-21 or 22-30) and Respect and Female and business and loan and woman

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How good is this collection? (40/100)
When making inferences, it is important to have many perspectives. Running our story quality test will measure this using the diversity of story collectors, named organizations, locations, and the balance in story point of view and tone to estimate the collection's suitability for meta-analysis.
Each component of the score is between 0 and 100, with 100 being the best and 0 being the worst.

Diverse sources: 2/100 (highest when collection contains hundreds of stories with miniminal overlap among the storytellers, named organizations, and locations)
Completeness: 91/100 (100% when every storytellers answer every question)
Diverse points of view: 0/100 (100% when there is a good balance of points of view. Personal experiences carry more weight over organization perspectrives.
Balance in story tone: 67/100 (100% if collection has a balance of positive and negative perspectives)
Your collection has a negative tone.

q2:A woman and her husband lived being poor. It came a time the woman started a small business and then joined a women marry-go round which made her also to get loan. She bought a land and posho mill which brought alot of income and they thank God for their prosper. But they had children who in primary school and they need their fully support. The husband then started drinking alcohol with the income they used to get which made the business to collapse.
Q3:EFFORTS
Q4:INDIVIDUAL
Q6:kenya
Q7:kitale east
Q8:sinereri
Q9:Less than 2 months ago
Q10:Female
Q12:Heard about it happening
Q13:The right people
Q15:Yes
Q17: <<2012-10-25-soweto-S#368-P#01>> _
Q18:Angry
Q19:Respect,Self-esteem,Freedom
Q23:50
Q37:Good idea that worked somewhat
Q38:Specific problem
Q39:Mixed
Q40:Mixed
Q122:22-30
Q132:None
Q133:None
Q134:Individual

q2:Mary came from USA five months ago. Mary comes from Vihiga district, kisangila village Mary has changed village women in her community.She came with idea that to train women in weaving traditional baskets and mats.They have open a woman group.They are exporting the baskets and mats to the project. women have been given loans fees to change and business to pay school fees to change and eradicate poverty in the community
Q3:EMPOWER WOMEN
Q4:Kwosi women group
Q6:Nairobi
Q7:Vihiga
Q8:Kisangula
Q9:2-6 months ago
Q10:Female
Q12:Saw it happen
Q13:The right people
Q15:Yes
Q17:<<2012-08-13-DAGORETI-S#092 -p#01>>_
Q18:Inspired
Q19:Food and shelter,Knowledge,Respect
Q37:Good idea that succeeded
Q38:Mixed
Q39:Social relations
Q40:Mixed
Q122:22-30
Q132:None
Q133:None
Q134:OSI's

Found 2 records.
mysql icon_filters: group_id between 0 and 5000 and q10 = 'Female' and q19 like '%Respect%' and q122 in ('16-21','22-30') and (( q2 like '%business%' and q2 like '%loan%' and q2 like '%woman%' ) or ( q3 like '%business%' and q3 like '%loan%' and q3 like '%woman%' ) or ( q4 like '%business%' and q4 like '%loan%' and q4 like '%woman%' ) or ( q5 like '%business%' and q5 like '%loan%' and q5 like '%woman%' ) or ( q6 like '%business%' and q6 like '%loan%' and q6 like '%woman%' ) or ( q7 like '%business%' and q7 like '%loan%' and q7 like '%woman%' ) or ( q8 like '%business%' and q8 like '%loan%' and q8 like '%woman%' ) or ( q11 like '%business%' and q11 like '%loan%' and q11 like '%woman%' ) or ( q17 like '%business%' and q17 like '%loan%' and q17 like '%woman%' ) or ( q26 like '%business%' and q26 like '%loan%' and q26 like '%woman%' ) or ( q27 like '%business%' and q27 like '%loan%' and q27 like '%woman%' ) or ( q28 like '%business%' and q28 like '%loan%' and q28 like '%woman%' ) or ( q29 like '%business%' and q29 like '%loan%' and q29 like '%woman%' ) or ( q35 like '%business%' and q35 like '%loan%' and q35 like '%woman%' ) or ( q41 like '%business%' and q41 like '%loan%' and q41 like '%woman%' ) or ( q42 like '%business%' and q42 like '%loan%' and q42 like '%woman%' ) or ( q43 like '%business%' and q43 like '%loan%' and q43 like '%woman%' ) or ( q46 like '%business%' and q46 like '%loan%' and q46 like '%woman%' ) or ( q47 like '%business%' and q47 like '%loan%' and q47 like '%woman%' ) or ( q60 like '%business%' and q60 like '%loan%' and q60 like '%woman%' ) or ( q65 like '%business%' and q65 like '%loan%' and q65 like '%woman%' ) or ( q70 like '%business%' and q70 like '%loan%' and q70 like '%woman%' ) or ( q71 like '%business%' and q71 like '%loan%' and q71 like '%woman%' ) or ( q72 like '%business%' and q72 like '%loan%' and q72 like '%woman%' ) or ( q73 like '%business%' and q73 like '%loan%' and q73 like '%woman%' ) or ( q74 like '%business%' and q74 like '%loan%' and q74 like '%woman%' ) or ( q75 like '%business%' and q75 like '%loan%' and q75 like '%woman%' ) or ( q76 like '%business%' and q76 like '%loan%' and q76 like '%woman%' ) or ( q77 like '%business%' and q77 like '%loan%' and q77 like '%woman%' ) or ( q80 like '%business%' and q80 like '%loan%' and q80 like '%woman%' ) or ( q81 like '%business%' and q81 like '%loan%' and q81 like '%woman%' ) or ( q86 like '%business%' and q86 like '%loan%' and q86 like '%woman%' ) or ( q87 like '%business%' and q87 like '%loan%' and q87 like '%woman%' ) or ( q88 like '%business%' and q88 like '%loan%' and q88 like '%woman%' ) or ( q89 like '%business%' and q89 like '%loan%' and q89 like '%woman%' ) or ( q98 like '%business%' and q98 like '%loan%' and q98 like '%woman%' ) or ( q99 like '%business%' and q99 like '%loan%' and q99 like '%woman%' ) or ( q110 like '%business%' and q110 like '%loan%' and q110 like '%woman%' ) or ( q111 like '%business%' and q111 like '%loan%' and q111 like '%woman%' ) or ( q116 like '%business%' and q116 like '%loan%' and q116 like '%woman%' ) or ( q117 like '%business%' and q117 like '%loan%' and q117 like '%woman%' ) or ( q123 like '%business%' and q123 like '%loan%' and q123 like '%woman%' ) or ( q125 like '%business%' and q125 like '%loan%' and q125 like '%woman%' ) or ( q132 like '%business%' and q132 like '%loan%' and q132 like '%woman%' ) or ( q133 like '%business%' and q133 like '%loan%' and q133 like '%woman%' ) or ( q134 like '%business%' and q134 like '%loan%' and q134 like '%woman%' ) or ( q135 like '%business%' and q135 like '%loan%' and q135 like '%woman%' ) or ( q136 like '%business%' and q136 like '%loan%' and q136 like '%woman%' ) or ( q138 like '%business%' and q138 like '%loan%' and q138 like '%woman%' ) or ( q141 like '%business%' and q141 like '%loan%' and q141 like '%woman%' ) or ( q142 like '%business%' and q142 like '%loan%' and q142 like '%woman%' ) or ( q151 like '%business%' and q151 like '%loan%' and q151 like '%woman%' )) LIMIT 4000;
filter_questions ['q10', 'q19', 'q122']
merge:ignore
A[leZXl7xqf529]:SUCCESS: 1 rows inserted.
not enough values to unpack (expected 3, got 2)