Crowdsourcing have been gaining increasing popularity as a highly distributed digital solution that surpasses both borders and time-zones. Moreover, it extends economic opportunities to developing countries, thus answering the call of impact sourcing in alleviating the welfare of poor labor in need. Nevertheless, it is constantly criticized for the associated quality problems and risks. Attempting to mitigate these risks, a rich body of research has been dedicated to design countermeasures against free riders and spammers, who compromise the overall quality of the results, and whose undetected presence ruins the financial prospects for other honest workers. Such quality risks materialize even more severely with imbalanced crowdsourcing tasks. In fact, while surveying this literature, a common rule of thumb can be indeed derived: the easier it is to cheat the system and go undetected, the more restrictive and across-the-board discriminating countermeasures are taken. Hence, also honest yet low-skilled workers will be placed on par with spammers, and consequently exposed and deprived of much-needed earnings. Therefore in this paper, we argue for an impact-driven quality control model, which fulfills the impact-sourcing vision, thus materializing the social responsibility aspect of crowdsourcing, while ensuring high quality results.