GCRI · Research library
Methods and technical papers
Papers describing the quantitative and qualitative methods of The Global Centre for Risk and Innovation (GCRI), open enough for another team to apply, test or criticise them. Each states the method, its assumptions, its validation, where it fails and the software or data it needs. Methods link to their STEM Commons pages. Part of the research library.
The workspace for methods and technical papers.
Evidence grading
Methods and technical papers · Review wave 2 · Example interface, not a record
Certainty by criterion
0 low to 100 high
Higher is more certain. Values are examples.
Review queue
- R-1Country programmes activesecond reviewer3 d
- R-2Evidence records with labelssecond reviewer2 d
- R-3Methods appliedsecond reviewer4 d
GCRI · Evidence grading. Evidence grading: example names and illustrative values, not a record.
Quantitative and qualitative, held to one standard.
Both kinds of method must be described well enough to be checked.
Models and statistics
Statistical, network, simulation and optimisation methods, such as uncertainty quantification.
Structured judgement
Interviews, expert elicitation, scenario workshops and case analysis, with policy and decision science.
Combined designs
Studies that combine both, stating how evidence of each kind is weighed.
The reporting guides a methods paper follows, by kind of method.
| Kind of method | Guide | Reference |
|---|---|---|
| Prediction models, including machine learning | TRIPOD+AI | Collins and others, BMJ, 2024 |
| Agent-based and other simulation models | ODD protocol, second update | Grimm and others, 2020 |
| Computer model calibration | Calibration with explicit model discrepancy | Kennedy and O’Hagan, 2001 |
| Machine-learning models in deployment | Model cards | Mitchell and others, 2019 |
| Land change from remote sensing | Area estimation from a reference sample | Olofsson and others, 2014 |
| Structured expert judgement | Documented elicitation protocol with calibration questions | Stated in each paper |
Reviewed methods are designed for the Methods and Technical series, with each model described under NS-12 of the proposed Nexus Standards. Methods researchers can contribute replications and documented failures; both are published.
How the process runs.
Process
Methods and technical papers: Source to evidence record
A source is registered and assessed, its record labelled and classified, and a Digital Evidence Passport carries provenance; a correction reaches every dependent record.
01 Intake
- 01Identify the sourceSource · The source stewardDiscovery is not ingestion. Source identification records the source class, steward, institutional role and jurisdiction.
- 03Register the sourceStepRegistration makes a discovered source a governed participant in the evidence lifecycle. It implies no endorsement or certification.
02 Assessment
- 04Jurisdiction and rights classificationGateRights classification identifies licence, consent, confidentiality and reuse limits. The result determines access and routing.
- 05Evidence quality assessmentStepQuality assessment decides whether a record is fit for a defined use. Quality is multidimensional and never a single score.
- 06Evidence labelStepEvery finding carries one of nine evidence labels. Missing data is shown as missing, never as zero.
03 Record
- 07Evidence classificationRecordClassification gives records lifecycle states, such as source-verified, public-safe or restricted. States can coexist and set evidence use.
- 08Source release and identifierRecord · NXS-GRX (canonical data, provenance and risk)A source release records rights, temporal coverage and source lineage. A UNI is allocated through a catalogue and never typed by hand.
- 09Digital Evidence PassportRecordThe passport records provenance, quality, rights, jurisdiction, permitted use and history, so evidence keeps its context when it travels.
04 Use and correction
- 02Correction reaches dependent recordsStep · NXS-GRX (canonical data, provenance and risk)A source correction propagates to dependent cuts and products without rewriting old results.
- 10Public-safe reviewGatePublic-safe review decides whether a record can be shared publicly. It is a distinct stage and is not validation.
Data protocols: evidence lifecycle · Data protocols: core data objects and evidence records · The Global Risks Index (NXS-GRX)Designed programme process, not a record of operation. No cycle has run and every programme is at service level R0. A gate is a programme decision, never a public-authority approval, certification or deployment authorisation. The data-protocol pages describe a design, not a running system.
Six elements in every paper.
- 01
Purpose
The question the method answers and where it should not be used.
- 02
Assumptions
What must be true for results to hold.
- 03
Validation
How the method was tested, on which data and with what result.
- 04
Failure modes
Known situations where the method gives misleading results.
- 05
Implementation
Software, versions and parameters, released where licences allow.
- 06
Review
Who reviewed the paper and its review state.
A method no one can check is not evidence.
Risk decisions often rest on models and judgement that are hard to see. Open methods let independent researchers, source agencies and affected communities test them, find weaknesses and propose improvements. Where licensing or security prevents releasing code or data, the paper says so and describes the method in enough detail to assess.
Where methods are applied.
Modelling practice: models and scenarios. Reviewed methods may appear in the Methods and Technical series. Experimental protocols: Labs protocols and research design. Quantitative methods for finance: The Global Risks Alliance (GRA), models and professional methods.
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
- Collins and others, TRIPOD+AI statement, BMJ 385 (2024)
- Grimm and others, The ODD protocol for describing agent-based and other simulation models: a second update, Journal of Artificial Societies and Social Simulation 23 (2020)
- Kennedy and O’Hagan, Bayesian calibration of computer models, Journal of the Royal Statistical Society Series B 63 (2001)
- Mitchell and others, Model cards for model reporting, Proceedings of FAT* 2019
- Olofsson and others, Good practices for estimating area and assessing accuracy of land change, Remote Sensing of Environment 148 (2014)
- NS-12, Software, model and AI/ML artefacts, a proposed standard
Questions and answers.
Why publish methods separately from results?
Are qualitative methods treated as less rigorous?
Can I reuse a GCRI method?
How are methods improved over time?
Can I propose an improvement to a method?
Contribute evidence from this page.
Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
- EmailGateway GW-GCRISend it from this pageEvidence, a correction, a case, a method, a review, a translation or profile input. It arrives tagged with this page, so the editor who owns it receives it.
- ZenodoOpen depositDeposit it and submit to the communityPublish your own record on Zenodo with your ORCID iD, then submit it to the Reports community there. Curators review it; the DOI stays yours.
- ReportsGCRISee every way to contributeThe contribution types, how each is reviewed, what credit you receive, the licence and what is never accepted.
Never send personal data, sensitive infrastructure detail or anything you have no right to share. A contribution is reviewed before use; sending it confers no authorship or editorial rights, and sponsorship never buys a finding.
Bring a method, a dataset or a question.
Information and enquiries are open today. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.
Legal documents.
Maps, data and figures on this site are shown for context and general information. They are not advice, forecasts or warnings; the limits of the information are set out in the Disclaimers and Data Notices.