Article summary
Separate +252 phone cleanup from gender-and-age validation, using labeled samples, unknown rates and group errors to decide whether fields fit a research purpose.
A Somalia WhatsApp Gender and Age project should not begin with “how do we obtain more labels?” It should begin with “how reliable are these labels on our own population?” Gender and age are personal-profile attributes. A structurally valid phone does not make a profile inference correct, and one aggregate match rate can conceal errors affecting diaspora records, particular sources or age groups.
Normalize against a current +252 plan first
Somalia’s National Communications Authority describes its management of numbering resources and separates fixed, mobile, emergency and other categories in the National Telephone Numbering Plan. Cleanup should retain raw value, country code, candidate use and rule version. The presence of +252 alone does not prove that a record is a mobile phone.
Country code is not current location
+252 places a phone in the Somali numbering system. It does not establish that the holder is currently in Somalia, nor does it establish city, language, nationality or purchasing power. In diaspora, aid, logistics and cross-border trade datasets, source_country, current_market and phone_plan_country should remain different fields.
Create a calibration sample before population use
Draw a sample from contacts who have voluntarily supplied an age band and gender where the intended use permits comparison. Do not request identity documents merely to test a screening result. Stratify the sample by source, collection period and business context so the evaluation does not consist only of the cleanest, easiest-to-match records.
A confusion table is more useful than “high accuracy”
| Measure | What it reveals | Common misuse |
|---|---|---|
| Return coverage | How many records receive a non-null result | Reported as accuracy |
| Unknown rate | How many cannot be classified or returned | Removed from the denominator |
| Stratified agreement | Agreement with known values by source or age band | Hidden behind one average |
| Mapping-collision rate | Inputs differing from mapped phones | Silently overwritten |
Validate age at the granularity the purpose needs
If an approved use only needs broad adult content preferences, there is no justification for pursuing an exact age. Define permissible bands first, then measure adjacent-band confusion and unknowns. Inferences of this kind should never determine consequential access involving children, employment, credit, insurance, healthcare or public services.
What WS Gender and Age can return
AIPUSH WS Gender and Age may return phone, age, gender, avatar and mapped WhatsApp phone. The avatar is part of the returned set but must not be expanded into claims about ethnicity, religion, health, politics or income. Gender also needs unknown and do-not-use states rather than being forced into a binary value.
Assign errors to the correct layer
- Number layer: format, length, use or country attribution is unresolved.
- Mapping layer: the relation between submitted and returned phone is unresolved.
- Visibility layer: a field was not returned or changed with time.
- Profile-agreement layer: a result differs from information supplied by the person.
Layered errors prevent a formatting failure from being counted as a gender-and-age failure and prevent an unknown observation from becoming a negative personal label.
Minimize TXT and preserve a withdrawal route
The TXT contains one phone per line and no name, image, age, gender or project identity. An internal calibration table rejoins through a random sample_id and records purpose, permission basis, check date, retention period and deletion state. If a person withdraws permission, both the contact record and derived observation can be located and retired.
Report each source separately
Website forms, event registrations, partner lists and legacy CRM differ in completeness and population composition. Report coverage, unknowns, agreement and collisions for every source. If a stratum is too small, state that evidence is insufficient; do not borrow performance from a different source.
Let the use case determine the acceptance threshold
Anonymous content research and individual marketing selection cannot share one threshold. Aggregated research still needs protection from small-cell identification. Individual use needs stronger permission, correction and contestability. If key strata show substantial distortion, the defensible result may be to exclude gender and age rather than tune the report toward an attractive overall number.
The quality of a Somalia project is not the number of populated gender and age cells. It is whether the team can explain how +252 phones entered the sample, where reference values came from, how unknowns and collisions were counted, and which decisions are explicitly out of scope.
