[nltk_data] Downloading package stopwords to /root/nltk_data... [nltk_data] Package stopwords is already up-to-date!
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 |
|---|---|---|
| 70.6666666666667 | Tamarandom#9361 | for the incredible work leading the OP migration and making sure everything gets completed on time |
| 32.3333333333333 | bear100#9085 | for the SWOT analysis in the fiduciary stewarship draft doc. It's cleat that dozens of hours of work went into that. |
| 29.6666666666667 | Tamarandom#9361 | for doing sooooo much work for the TEC on top of all the other responsibilities she has in her work and life! |
| 27.0 | natesuits#4789 | for his leadership and ownership in driving the new TEC events series |
| 25.3333333333333 | Tamarandom#9361 | for her amazing collaboration in working through the different social components (a lot of docs) of the OP migration |
| 25.3333333333333 | Griff (💜,💜)#8888 | for supporting so much the TEC through this month! |
| 23.6666666666667 | natesuits#4789 | for all the twitter spaces |
| 23.6666666666667 | natesuits#4789 | for preparing and hosting X-spaces for the gitcoin round. You are always so well prepared, eloquent and knowlegeable. You represent the TEC well in these public endeavors. |
| 22.6666666666667 | bear100#9085 | for his remarkable work on the Fiduciary Stewardship policies that he has been pulling together for the Common Pool and the TEC's overall financial sustainability and health. A ton of work and really great thinking! |
| 22.6666666666667 | 🐙 octopus#5508 | for maintaining the Bonding Curve Research Group and opening up public education with luminaries such as Mark Richardson, of Bancor and Carbon. |
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 109 praises, 10 (9.17%) 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 109 praises, 0 (0.00%) 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.