---
title: R- Knowledge Synthesis- a conceptual model and practical guide
enableToc: false
creation date: $=dv.current().file.ctime
last modified date: $=dv.current().file.mtime
author: "[[P- Joel Chan.md|P- Joel Chan]]"
year: 2020
reference: "https://oasislab.pubpub.org/pub/54t0y9mk/release/2"
tags:
- resource
status: 
alias: "@chanKnowledgeSynthesisConceptual2020"
---


https://oasislab.pubpub.org/pub/54t0y9mk/release/2

## Challenges and desiderata for a synthesis system

Here are some common failure modes for a synthesis system and process that I have experienced and observed in others (not mutually exclusive!):

1.  **Too much detail** (too low-level, missing forest for trees). This manifests as a lack of higher-level synthesis of what a collection of results means. A common manifestation is the “x said this, y said this, z said this” form of literature review.
    
2.  **Too little detail** (too high-level, missing the devil/diamonds in the details). This manifests as overgeneralization of claims, or glossing over critical inconsistencies or contradictions. A good example of this is debates about the role of “children” in COVID-19 transmission that ignore the details of differences between young children (under 10).
    
3.  **Insufficient context**. This is related to the lack of details, but separate in that context can also come from connection to other claims: if this is missing, even observation notes can be lost because their significance isn’t recognized.
    
4.  **Information silos.** This manifests in part also due to inordinate detail-orientedness, where important connections across disciplines or topics are ignored. This can also come from too little detail! If results are described at too high a level, we might miss important connections at the subproblem level between problems and results.
    
5.  **Information overload**. There are often too many papers to read and process in a rigorous and iterative way, which leads to / exacerbates the preceding set of problems!