[nltk_data] Downloading package stopwords to /root/nltk_data... [nltk_data] Unzipping corpora/stopwords.zip.
This document processes the outputs of the praise reward system and performs an analysis of the resulting token reward distribution.
Since praise gets valued on a scale, we can take a look at how often each value of the scale gets assigned by quantifiers. Note: This metric disregards scores of praise marked as a duplicate, since the score of the original is already being taken into account.
The ten highest rated contributions for this round were the following:
| Avg. score | To | Reason |
|---|---|---|
| 36.0 | danlessa#2831 | for his incredible support in writing the TE Stakeholder Study proposal. Danilo was proactive and generous with his time and knowledge and brought so much quality to our process which will directly impact the results if the proposals is approved. |
| 28.75 | roro#1166 | for a very good article on using both Machinations and cadCAD in the TE process https://medium.com/@e4rohan/a-conversion-guide-for-machinations-and-cadcad-ca94ccdea174 |
| 23.5 | Marathonmind#3078 | for hosting a really interesting twitter space about the Reputation Research Group, well done team! |
| 22.57 | Marathonmind#3078 | for presenting in the Rewards Systems Open Research Group Twitter Spaces where we covered Reputation & Expertise within web3. Thanks again for an amazing conversation and education session! |
| 22.25 | itsyaboi#2591 | for shipping the final version of the praise quantifier and getting it live on youtube! |
| 18.25 | divine_comedian#5493 | organizing the quantifier session, walking me through Praise's analytics and answering all my questions - I learned a lot and it was amazing!! 🔥 |
| 17.0 | natesuits#4789 | for hosting a really interesting twitter space about the Reputation Research Group, well done team! |
| 17.0 | bear100#9085 | for an awesome work-session on developing a comprehensive framework for a reputation system for the TE Community. |
| 15.75 | JHennyArt#2106 | for hosting a really interesting twitter space about the Reputation Research Group, well done team! |
| 15.0 | GideonRo#3175 | for his nonstop support and kindness. It's an absolute honor to work with someone so genuine. |
We can now take a look at the distribution of the received praise rewards. You can toggle the inclusion of the different sources by clicking on the legend.
We can also take a look at the amount of praise different users gave.
Now for something more fun: let's surface the top "praise flows" from the data. Thanks to @inventandchill for this awesome visualization! On one side we have the top 15 praise givers separately, on the other the top 25 receivers. The people outside the selection get aggregated into the "REST FROM" and "REST TO" categories.
Now let's take a closer look at the quantification process and the quantifiers:
To aid the revision process, we highlight disagreements between quantifiers.
This graphic visualizes controversial praise ratings by sorting them by the "spread" between the highest and lowest received score.
Please keep in mind that this is a visual aid. If there are several praise instances with similar spread and quant score, all but one end up "hidden" on the chart. For an exhaustive list, take a look at the exported file "praise_outliers.csv" .
Let's see how different quantifiers behaved by showing the range of praise scores they gave.
To interpret the box plot:
Bottom horizontal line of box plot is minimum value
First horizontal line of rectangle shape of box plot is First quartile or 25%
Second horizontal line of rectangle shape of box plot is Second quartile or 50% or median.
Third horizontal line of rectangle shape of box plot is third quartile or 75%
Top horizontal line of rectangle shape of box plot is maximum value.
Among 30 praises, 4 (13.33%) do not agree on duplication
Praise instances with disagreements in duplication are collected in 'results/duplication_examination.csv'. To compare, look at the last 4 columns: 'DUPLICATE MSG 1/2/3' and 'ORIGINAL MSG'.
Among 30 praises, 8 (26.67%) do not agree on dismissal
Praise instances with disagreements in dismissal are collected in'results/dismissal_disaggreed.csv'. You can further look into who dismissed and who did not.