Stories from Globalgiving Storytelling Project

Search Again Compare Tutorial Learn more Story View

2 stories matched Female and Under 16 and Respect and job and community

Your search did not match enough stories to do a complete analysis. Only basic information is showing.

How good is this collection? (52/100)
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: 94/100 (100% when every storytellers answer every question)
Diverse points of view: 70/100 (100% when there is a good balance of points of view. Personal experiences carry more weight over organization perspectrives.
Balance in story tone: 44/100 (100% if collection has a balance of positive and negative perspectives)
Your collection has a very negative tone.

q2:I came from a poor community and our family can not provide the food for almost 10 pple in the hous each day. In slums children and adults are struggling to get the job or anything which they can for them to succeed. In the street there are many challenges that we face every day a lot of cases are reported because of rape many girls are rapped now and then in the dark curlprits and they cannot do anything. But I believe that one day someone woll come and help them out. In the slums of Kibera the place is so dirty that many people like staying in a clean places and they cannot do so the place the people are leaving there is no any toilets around it is just flying papers which you can see on the iron sheets. But the organization has come up with something
Q3:Cleaning the Streams
Q4:AMANI KIBERA
Q6:Kenya
Q7:Nairobi
Q8:Langata
Q9:Less than 2 months ago
Q10:Female
Q12:Unknown
Q13:Mixed
Q15:Unknown
Q17:<<[HORIZON MOST DATA NOT FILLED]2012-10-17-SIAYA/KIBERA-S#190-P#00>>_
Q18:Unknown
Q19:Family and Friends,Respect,Fun
Q23:12
Q37:Unknown
Q38:Specific problem
Q39:Mixed
Q40:Mixed
Q122:Under 16
Q132:None
Q133:None
Q134:Amani

q2:In our community there was a woman whose name was kiundu he has a wife whose name was Rosa and his daughter so one day when kiundu went for a job she left Rosa and his daughter at home when it reached at 1:00am Rosa started preaparing alunch so in a split of a second she used a stove to cook with so the house caught fire and all the things were burnt so a person tried to help them.
Q3:The rescue
Q4:Individual
Q6:Nairobi
Q7:District
Q8:Village
Q9:2-6 months ago
Q10:Female
Q12:Was affected by what happened
Q13:The wrong people
Q15:Yes
Q17:<<2011-05-12-EAST AFRICA MEDIA GROUP-S#200-P#001>>_
Q18:Horrible
Q19:Physical Needs,Respect,Freedom
Q23:-1
Q37:Good idea that failed
Q38:Specific solution
Q39:Social relations
Q40:Lasting change
Q122:Under 16
Q132:0.31628000000000
Q133:32.58219000000000
Q134:Individual

Found 2 records.
mysql icon_filters: group_id between 0 and 5000 and q10 = 'Female' and q19 like '%Respect%' and q122 = 'Under 16' and (( q2 like '%job%' and q2 like '%community%' ) or ( q3 like '%job%' and q3 like '%community%' ) or ( q4 like '%job%' and q4 like '%community%' ) or ( q5 like '%job%' and q5 like '%community%' ) or ( q6 like '%job%' and q6 like '%community%' ) or ( q7 like '%job%' and q7 like '%community%' ) or ( q8 like '%job%' and q8 like '%community%' ) or ( q11 like '%job%' and q11 like '%community%' ) or ( q17 like '%job%' and q17 like '%community%' ) or ( q26 like '%job%' and q26 like '%community%' ) or ( q27 like '%job%' and q27 like '%community%' ) or ( q28 like '%job%' and q28 like '%community%' ) or ( q29 like '%job%' and q29 like '%community%' ) or ( q35 like '%job%' and q35 like '%community%' ) or ( q41 like '%job%' and q41 like '%community%' ) or ( q42 like '%job%' and q42 like '%community%' ) or ( q43 like '%job%' and q43 like '%community%' ) or ( q46 like '%job%' and q46 like '%community%' ) or ( q47 like '%job%' and q47 like '%community%' ) or ( q60 like '%job%' and q60 like '%community%' ) or ( q65 like '%job%' and q65 like '%community%' ) or ( q70 like '%job%' and q70 like '%community%' ) or ( q71 like '%job%' and q71 like '%community%' ) or ( q72 like '%job%' and q72 like '%community%' ) or ( q73 like '%job%' and q73 like '%community%' ) or ( q74 like '%job%' and q74 like '%community%' ) or ( q75 like '%job%' and q75 like '%community%' ) or ( q76 like '%job%' and q76 like '%community%' ) or ( q77 like '%job%' and q77 like '%community%' ) or ( q80 like '%job%' and q80 like '%community%' ) or ( q81 like '%job%' and q81 like '%community%' ) or ( q86 like '%job%' and q86 like '%community%' ) or ( q87 like '%job%' and q87 like '%community%' ) or ( q88 like '%job%' and q88 like '%community%' ) or ( q89 like '%job%' and q89 like '%community%' ) or ( q98 like '%job%' and q98 like '%community%' ) or ( q99 like '%job%' and q99 like '%community%' ) or ( q110 like '%job%' and q110 like '%community%' ) or ( q111 like '%job%' and q111 like '%community%' ) or ( q116 like '%job%' and q116 like '%community%' ) or ( q117 like '%job%' and q117 like '%community%' ) or ( q123 like '%job%' and q123 like '%community%' ) or ( q125 like '%job%' and q125 like '%community%' ) or ( q132 like '%job%' and q132 like '%community%' ) or ( q133 like '%job%' and q133 like '%community%' ) or ( q134 like '%job%' and q134 like '%community%' ) or ( q135 like '%job%' and q135 like '%community%' ) or ( q136 like '%job%' and q136 like '%community%' ) or ( q138 like '%job%' and q138 like '%community%' ) or ( q141 like '%job%' and q141 like '%community%' ) or ( q142 like '%job%' and q142 like '%community%' ) or ( q151 like '%job%' and q151 like '%community%' )) LIMIT 4000;
filter_questions ['q10', 'q19', 'q122']
merge:ignore
A[UERnkLjJxbY1]:SUCCESS: 1 rows inserted.
not enough values to unpack (expected 3, got 2)