[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 |
|---|---|---|
| 61.0 | pat.zip (TE Academy)#5266 | for all her work at the TEA and the TE community. You'll be missed around. |
| 55.0 | 0xr3x | for the migration of all our dashboards to Dune V3! |
| 48.0 | Griff (💜,💜)#8888 | For funding me for three months to work at TEC. I've created a proposal here to continue the work via TAO voting: https://forum.tecommons.org/t/4-month-te-data-science-fellowship/1287/3 |
| 41.0 | 0xr3x | For bringing 1hive to dune so that we can have tighter insights on TEC secondary markets and for being an all around Dune wizard. |
| 36.6666666666667 | Isaac (enti)#1546 | for managing the TEGR2 and pushing it to completion. |
| 35.6666666666667 | gideonro#0 | for many things but epsecially for a quiet and very effective leadership style that brings out the best of everyone you interact with. |
| 34.0 | Isaac (enti)#1546 | presenting the TEC so well at ETH Argentina. You did a tremendous job with the delivery and content. |
| 32.3333333333333 | 🐙 octopus#5508 | for his presentation on "Adversarial and Other Viewpoints on Augmented Bonding Curves" at the Bonding Curves Research Group! Loved it |
| 28.0 | gideonro#0 | for creating a marketing playbook for the upcoming TE grants round. This will definitely make thing easier |
| 25.3333333333333 | Tamarandom#9361 | for working hard to get ABCs to work on Optimism. Migrating to Optimism will be huge for us I think, and their work to make it a reality is what will make it possible |
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 92 praises, 20 (21.74%) 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 92 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.