Research Literature Synthesis
Synthesise a body of research into what is known and contested.
A synthesis of the evidence with consensus, dispute and the gaps.
Literature syntheses fail by flattening — treating twelve studies as twelve equal votes — so the Statistician weighs the evidence by its methods, sample sizes and power rather than its conclusions, while the Deep Researcher maps where the field actually stands and what the citation trails reveal. The Data Skeptic hunts the claims that outrun their data and the conflicts nobody declared, the Steelman ensures the minority position gets its best argument rather than a caricature, and the First-Principles Thinker with the Synthesiser turn the mess into a structure a decision can rest on: known, contested, unknown.
Help me synthesise the research on a topic into a clear picture of what is known, contested and unknown. THE TOPIC / QUESTION: [the research question or area] THE SOURCES: [paste abstracts, papers, findings, or describe the literature you have] WHY I NEED THIS: [the decision or work it informs] Debate: 1. The points of genuine consensus across the evidence. 2. The active disputes, and the strongest case on each side. 3. The quality and limits of the evidence — sample sizes, methods, conflicts, replication. 4. Where claims outrun the data. 5. The open questions and the gaps in what is known. 6. What this means for the decision or work it informs. FINAL SYNTHESIS: - A structured synthesis: what is well-supported, what is contested, what is unknown. - The confidence level on the main claims. - The most important caveat and the key gap to fill.
An illustrative excerpt for a hypothetical brief of this type: A product team is synthesising the research on four-day work weeks before piloting one, working from a stack of trial reports, company case studies and media summaries. Every real run is generated fresh from your specifics, debated across rounds, and audited before the verdict.
Sort the stack by evidence class before reading a single conclusion: a handful of coordinated trials with defined measures, a larger pile of self-selected company case studies, and media summaries of both. The trials support cautious claims about wellbeing and retention. The case studies cannot support causal claims at all — companies that volunteer for pilots and publish results are precisely the companies where it was already likely to work. That selection effect is the field’s biggest confound and most write-ups never mention it.
The headline claim — “productivity maintained or improved” — deserves its own audit: in most of the underlying studies, productivity is self-reported or proxied by revenue over short windows, and the pilots run inside a novelty period with public commitment effects. That does not make the claim false; it makes it weaker than its citation count. The synthesis should grade it “suggestive, measurement-limited”, not “established”.
The sceptical minority in the literature deserves its strongest form, not a dismissal: the compressed-hours critique — that some implementations intensify work rather than reduce it, with costs that surface after the study window closes — is raised by serious researchers, not just resisters. And the sector question is real: the evidence base over-represents knowledge work. A synthesis this team can trust must state where their situation falls outside the evidence.
Structure for the decision: well-supported — wellbeing and retention effects in knowledge-work settings, short horizon. Contested — durability past the novelty period, and productivity measurement itself. Unknown — effects in coverage-based and client-facing roles, which is half of this team. Practical consequence: the pilot should define its own productivity measures before it starts, because the literature will not settle that argument for them.
Prefer drop-and-go? Use the Research Literature Synthesis tool — team pre-seated, included with Plus.
Do I need to provide the papers, or does the council know the literature?
Provide what you have — abstracts, key findings, links, even a rough list — and state the question the synthesis must serve. The council reasons hardest about the structure and quality of evidence you provide; where it draws on general knowledge, the deliverable marks those claims for verification rather than presenting them as settled.
What does the confidence grading in the output look like?
Claims are sorted into well-supported, contested and unknown, with the reason for each placement — sample sizes, design limits, selection effects, replication status. The most useful line for most decisions is the honest “unknown” list: knowing what the literature cannot tell you prevents borrowing certainty that is not there.
Can it handle a field where the studies genuinely disagree?
Disagreement is where the format earns its keep: the Steelman argues each side’s best case, the Statistician examines whether the disagreement is methodological (different measures, populations, windows) or substantive, and the synthesis says which. Many famous “contradictions” dissolve into definition differences — and some do not, and you need to know which kind you have.