> For the complete documentation index, see [llms.txt](https://tagger.gitbook.io/tagger-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://tagger.gitbook.io/tagger-documentation/tokenomics/task-reward-calculation/data-review-and-staking.md).

# Data Review and Staking

Data Review operates within a community-driven framework, forming a key pillar of a scalable autonomous DeCorp model. Unlike traditional corporate hierarchies, which see managerial and operational costs rise exponentially with workforce expansion, this system scales without such inefficiencies. It follows the principles of true decentralization where verification and validation are maintained through a peer-to-peer structure, eliminating reliance on centralized oversight.

### **What is deemed a "correct" Data Review attempt?**

Each manually labeled data submission undergoes a first-round review by **three** assigned reviewers. A piece of manually labeled data will be:

Passed within the first round if:

* All three first-round reviewers unanimously accept the data labeling attempt.

Denied within the first round if:

* At least **two** first-round reviewers deny the data labeling attempt.

Passed on to a single second-round reviewer if:

* Exactly **two** first-round reviewers accept the data labeling attempt and one first-round reviewer denies the attempt.

In the case where a piece of data is passed on to the single second-round reviewer, their decision will represent the final outcome of the data review.

To maintain objectivity, reviewers are never informed whether they are acting in the first or second round for any given piece of data.

A **correct data review attempt** is therefore deemed as the case where the reviewer's decision aligns with the **final outcome** of the reviewed data. For example, if a piece of data is ultimately denied, any reviewer who selected "yes" will be marked as having made an incorrect attempt.

### Staking and Eligibility

To stand in the data reviewer set, a participant stakes an amount of $TAG greater than the **eligibility threshold** displayed in "Staking Management". From the pool of stakers who meet or exceed that bar, **n** addresses are drawn at random. **n** represents the number of data reviewers assigned per review window. The value **n** scales with the volume of data awaiting review, so the network always assigns just enough eyes - never too few, never too many. Review windows open and close based on the volume of data requiring review, with prior notice given.

The **minimum staking requirement** to become a data reviewer is displayed on the Staking Management page in the dashboard. The staking duration is a minimum of 7 days, and unstaking requires a 7-day waiting period after a request is made. Upon making a request to unstake, one may no longer remain as a data reviewer until the end of the review window.

Importantly, staking serves only as a prerequisite for data review eligibility and **does not generate DeFi-like returns**. This ensures that only highly committed reviewers participate, acting as the final safeguard against inaccurate data labeling.

**Reward Calculation Formula**\
\&#xNAN;*$TAG = single task reward × halving coefficient × account level coefficient × daily review accuracy coefficient*

**Single Task Reward**\
Rewards vary across data catalogs, with each assigned based on its difficulty and complexity. These rewards will be displayed in the Task Plaza before users select their tasks.

**Account Coefficient**\
Refer to the "How to Earn $TAG" section.

**Halving Coefficient**\
Refer to the "Token Distribution" section.

**Daily Review Accuracy Coefficient**

Daily Review Accuracy reflects the percentage of data review tasks that is deemed as "correct attempts" on the previous working day.

| Daily Review Accuracy Coefficient | Daily Review Accuracy % (x) |
| --------------------------------- | --------------------------- |
| 0                                 | x < 30%                     |
| 0.3                               | 30% ≤ x < 50%               |
| 0.5                               | 50% ≤ x < 70%               |
| 0.8                               | 70% ≤ x < 85%               |
| 1.0                               | 85% ≤ x < 90%               |
| 1.2                               | 90% ≤ x < 95%               |
| 1.5                               | x ≥ 95%                     |


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