Knowledge is made by asking, gathering, analysing, checking and publishing. Digital systems collapsed the cost of gathering and analysing, left checking exactly where it was, and that gap is the story.
Where the cost of making knowledge collapsed, and the one step that did not move.
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The genuine gains
- Instruments produce more in a day than a career's work used to, in fields from astronomy to genetics.
- Analysis that took years is now a computation, which is how protein structures came to be predicted at scale.
- Collaboration crosses borders, so a question can be worked on continuously by people in different time zones.
- Publication is immediate, so a result reaches other researchers in days rather than in a year.
Real-world examples you can name
AlphaFold and the protein structure database — AlphaFold 2 published 2021; over 200 million predicted structures released July 2022
A machine-learning system that predicts a protein's three-dimensional shape from its amino-acid sequence, a problem that had resisted fifty years of work. The predictions were released openly rather than licensed.
Who it affected: Biologists worldwide, including laboratories that could never afford the equipment to determine structures experimentally.
The Bletchley Declaration on AI safety — November 2023
The first international statement on frontier AI risk signed by both the US and China, agreeing that the most capable models pose risks worth coordinating on. It is a declaration of intent, with no enforcement.
Who it affected: Governments, model developers, and anyone hoping governance keeps pace.
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Checking is the bottleneck: Reproducing a result takes the same work it always did, and almost nobody is rewarded for doing it.
So results are now produced faster than they can be checked, and wrong ones stay in the record while later work is built on them.
| Practice | What it makes possible | Why it is not standard |
|---|---|---|
| Publish the data | Somebody else can repeat the analysis | It is work with no reward attached |
| Publish the code | The exact steps can be rerun | Code written for one paper is rarely tidy |
| Register the method first | Stops the question being changed to fit the result | It removes flexibility researchers value |
How this is tested — this topic gives you a precise, unusual argument about evidence that transfers to Paper 2 source evaluation. It comes up two ways:
Paper 1 — structured question
- Part c: evaluate the effect of digital tools on research
- Part b: explain why more results is not more knowledge
Paper 2 — source-based question
- Q1 and Q2: evaluate a source's evidence
- HL Paper 3: judge the strength of the evidence in a statement
The trap: listing achievements: Faster discovery is the easy half. The examinable point is that production outran checking, and what follows from that.
Explain one reason why producing more research results does not always produce more knowledge.
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.
Recommend two changes that would make research results more trustworthy, and justify them.
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.