Crowd workers are distributed and decentralized. While decentralization is designed to utilize independent judgment to promote high-quality results, it paradoxically undercuts behaviors and institutions that are critical to high-quality work. Reputation is one central example: crowdsourcing systems depend on reputation scores from decentralized workers and requesters, but these scores are notoriously inflated and uninformative. In this paper, we draw inspiration from historical worker guilds (e.g., in the silk trade) to design and implement crowd guilds: centralized groups of crowd workers who collectively certify each other's quality through double-blind peer assessment. A two-week field experiment compared crowd guilds to a traditional decentralized crowd work model. Crowd guilds produced reputation signals more strongly correlated with ground-truth worker quality than signals available on current crowd working platforms, and more accurate than in the traditional model.
Today, the intellectual inheritance of guilds persists via professional organizations, which replicate some of their benefits @cite_20 . Professions such as architecture, engineering, geology, and land surveying require varying lengths of apprenticeships before one can gain professional certification, which holds great legal and reputational weight @cite_0 @cite_47 . However, these professions fall into traditional work models and do not cater for global and location-independent flexible employment @cite_26 . Non-traditional work arrangements such as freelancing do not provide common work benefits such as economic security, career development, training, mentoring and social interaction with peers, which are legally essential for classification as full-time work in many cases @cite_40 . Although the support of professional organizations exists for freelance work, much of the underlying value of guilds does not.
@cite_26 @cite_0 @cite_40 @cite_47 @cite_20
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Guilds are social scientists’ favoured historical example of institutions generating a ‘social capital’ of trust that benefited entire economies. This article considers this view in the light of empirical findings for early modern Europe. It draws the distinction between a ‘particularized’ trust in persons of known attributes and a ‘generalized’ trust that applies even to strangers. This is paralleled by the distinction between a ‘differential’ trust in institutions that enforce the rights of certain groups and a ‘uniform’ trust in impartial institutions that enforce the rights of all. Guilds had the potential to generate the particularized and differential trust to solve market failures relating to product quality, training, and innovation, although the empirical findings suggest that they often failed to fulfil this potential. Guilds also had the potential to abuse their trust, and the empirical findings show that they indeed manipulated their social capital of shared norms, common information, mutual sanctions, and collective political action to benefit their members at others’ expense, blocking the spread of generalized and uniform trust. Counter to the assumptions of social capital theory, the example of pre-industrial guilds suggests that the particularized and differential trust fostered by associative institutions do not favour but hinder the generalized and uniform trust fostered by impartial institutions. What if, rather than relying on an employer or the government to meet their human needs, individual workers joined independent organizations whose primary purpose was to provide stable "homes" as they moved from job to job? We call these organizations "guilds" by analogy to the craft associations of the Middle Ages, and in this paper we examine what they might do and how they might emerge. For decades large corporations as well as labor placement services have maintained extensive yet static resume databanks. Online professional networks like LinkedIn have taken these resume databanks to a dynamic, constantly updated and massive scale professional profile dataset spanning career records from hundreds of industries, millions of companies and hundreds of millions of people worldwide. Using this professional profile dataset, this paper attempts to model profiles of individuals as a sequence of positions held by them as a time-series of nodes, each of which represents one particular position or job experience in the individual's career trajectory. These career trajectory models can be employed in various utility applications including career trajectory planning for students in schools & universities using knowledge inferred from real world career outcomes. They can also be employed for decoding sequences to uncover paths leading to certain professional milestones from a user's current professional status. We deploy the proposed technique to ascertain professional similarity between two individuals by developing a similarity measure SimCareers (Similar Career Paths). The measure employs sequence alignment between two career trajectories to quantify professional similarity between career paths. To the best of our knowledge, SimCareers is the first framework to model professional similarity between two people taking account their career trajectory information. We posit, that using the temporal and structural features of a career trajectory for modeling profile similarity is a far more superior approach than using similarity measures on semi-structured attribute representation of a profile for this application. We validate our hypothesis by extensive quantitative evaluations on a gold dataset of similar profiles generated from recruiting activity logs from actual recruiters using LinkedIn. In addition, we show significant improvements in engagement by running an A B test on a real-world application called Similar Profiles on LinkedIn, world's largest online professional network. Crowdsourcing offers new forms of work arrangements enabled and facilitated by the advancements in Internet technologies and growing popularity of social media. However, do these new forms of work empower workers to craft their own careers or do they create a sweatshop where workers complete fragmented tasks to earn minimal pay? We posit that career theories and job crafting approaches collectively provide valuable theoretical perspectives for examining this question. By assessing the degree to which these platforms afford or constrain the workers to exert their personal agencies (i.e., affords career and job crafting preferences), we argue, will partially determine whether these new forms of work are a harbinger of worker empowerment or exploitation. Preliminary findings of this exploratory research-in-progress, conducted using two types of workers on Amazon-Mechanical-Turk, reveal that these new forms of work arrangements have a potential for both empowerment and exploitation of workers.