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2020-08-14-coding-prd-3-2.txt
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2020-08-14-coding-prd-3-2.txt
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GSoC Meetings
GSoC 2020: Implement the Social Currency Metrics System in GrimoireLab
Coding period 3: Week 10
1. Discussion about the previous week
1. Tag data for CHAOSS SCMS sheet
2. PyCon Proposal submitted
3. Dashboard ready for CHAOSS
4. Executing onion study to have clearly defined roles for each user.
5. Discussing the approach to drill down into subcategories. (only while converting Excel to ES)
2. Discussion/Calibration
1. Weights?
1. Weights- Relevance/Happy
2. Visualization of weight per currency
2. Convert utility and transparency to merit.
3. Changing historical comments
4. Trust-
1. long term relationship
2. Internal Merit (Affiliation)
5. Utility +Merit = Trust
6. Workflow vs Technology vs User Experience :
1. Technology- Software Development Requests and All feature requests
7. CHAOSS Mission -> future of CHAOSS
8. Low-frequency tags:
1. Solution1: Data table of categories: indicating the frequency of categories
2. Commercial Brands: get rid of them
3. OSS: low trends matter more: cant get rid of them
4. Solution: Pie chart of categories classifying as others until it becomes a significant category.
9. Communication: Sounds general: removed
10. [UX] A view in Google Sheet: indicating all records with Invalid category.
11. [UX]: A view in GSheet filtering date
3. Analysing Trends and Patterns: Dashboard
1. Utility reached its peak in Oct’2019, having a value of 12. At all other times, the value of utility has been around 2-3. - Indicating an odd utility value, and measures could be taken to make utility more uniform!
2. Transparency has been fairly consistent throughout the last 2 years. (Kudos!)
3. The value of all other social currencies like Trust, Merit, Consistency has been very low throughout, the value moves around 1-2 in 1 month. Could work on converting utility and transparency towards Merit!
4. We found 42.86% of emails categorised as Administrative, and only 14% of mails involve community content. Increasing contributor participation is imperative!
5. IRC conversations involve more technical support discussions(32%) or interpersonal discussions(owing to it being a chat system)- (24%)
6. 38% of tweets are about community content (owing to the nature of the forum) and 42% of tweets are about Conferences.
7. In all platforms, we can see that +ve weights overweigh negative weights, meaning mostly community discussions are relevant to projects and other people involved. (Kudos!)
8. Also, we see the “Transparent” as the most popular social currency of the CHAOSS community.
9. In the end, These assessments are recorded from 200 conversations only, as more tags come in, we’ll get more clarity towards the community’s nature.
4. Next steps, action items, and tasks:
1. Weekly interactions in Chaoss community.
2. Tag records for calibration
3. Consolidate scripts for final evaluation
4. Fix Bug for onion study & adding subcategories as per codex.