Prism Levels

In order to communicate "reputation"/"caliber"/"likelihood to succeed," we propose a system that quantifies various criteria as a proxy for trust. The literal output will likely be a numerical score but should not be presented as such. We believe that segmenting users into “levels” might be more appropriate.

We definitively want to avoid creating an exclusionary, potentially biased or harmful, system that conjures notions of a Black Mirror episode. As such, we will have to think carefully about the criteria that encapsulates “reputation” and the messaging/presentation of results.

Levels should enable comparison of disparate talent along the hallmarks of what makes a "good freelancer," such as responsiveness, time to completion, etc. and not purely on performance or self-populated . The data informing levels should also be triangulated from several sources to reduce bias. In sum, levels should be a first-pass indicator of "good enough," one data point taken into consideration to establish baseline expectations.

Criteria to consider for levels:

• No. of jobs completed, talent hired, referrals converted

• Score of referrals successfully hired

• Reviews by clients or talent

• Community support (e.g. hosting online webinar)

Furthermore, the quantification of reviews should also take into account multiple criteria to be perceived as objectively "fair" rather than a subjective opinion. Criteria should be broad enough to apply to a range of freelance talent from graphic designers to developers. High level criteria to consider include: speed to reply, completion rate, timeliness, etc.

Users will be rewarded for advancing levels, which encourages platform-building activity. Rewards may include tokens, access to new opportunities, access to in-demend talent, fee discounts, higher staking interest, etc. Special NFTs or other tokens can also be unlocked/rewarded to signal reputational information, such as “Connector of the Week” or “Top 10 Talent Mentor.”