Four ways to enter MCRP
These are synthetic teaching cases for a public research prototype, dated 2026-09-25. They make no claims about actual people, experiments, lawsuits, or institutional endorsement. All numerical data and organizations are invented. The proposed common interface is offer → check → rely → amend. Different communities retain their own standards of evidence and decision authority.
| Audience | Start with a question | Worked case | Small first contribution |
|---|---|---|---|
| Physics and astronomy | Which conclusions share this calibration? | Shared calibration | Reconstruct one number and name the untested measurement assumption |
| Biology | Did the experiment identify the effect? | Batch, assay, replication | Map sample, batch and treatment before rerunning the statistics |
| Economics and social science | What population and estimand does this estimate concern? | Identification and transport | Compare one preregistered estimand with one reported estimand |
| Law | Who can decide what, by when, with what remedy? | Contested interpretation and deadlines | Separate an evidence check from an institution’s decision |
Human entry
Choose one case, read its opening situation, and fill four sentences before seeing JSON: “I offer…”, “I checked…”, “I would rely on this for…”, “I would reconsider if…”. A facilitator then introduces the planted failure. The exercise succeeds when the participant narrows an unsupported use or names missing evidence; it does not require an affirmative verdict. Each case contains an answer key and a longer clinic. Onboarding time and supervision count as real labor.
An established collaboration can import its existing release and review letter. A lone researcher can contribute a one-claim numerical check. Neither must reorganize its internal workflow. Public disclosure, authorship, and a relying institution’s authority remain explicit human decisions.
Implementer entry
The independent, standard-library domain oracle is executable immediately:
python3 papers/07-release-packet/domains/fixtures/domain_models.py
python3 papers/07-release-packet/domains/fixtures/replay_receipts.py
python3 -m unittest discover -s papers/07-release-packet/domains/fixtures -p 'test_*.py'scenarios.json contains semantic scenario fixtures, not
wire-protocol conformance vectors. Its declared schema separates
immutable target content, human-readable receipt narratives, expected
domain calculations, and failure-injection goals. The toy-agent
framework in ../toy_agents/ provides a separate receipt
engine; using both does not turn either into a production scientific or
legal service. Fixture identifiers and control groups are trusted
inputs. No identity discovery, signature verification, confidential-data
handling, or real decision is executed.
Every adapter must preserve scope, limits,
exact target identity, and authority kind. An adapter that can represent
only scientific and publication reliance must
refuse a legal operational-decision type; it must not relabel that
decision as scientific to get through the API. Domain-specific semantic
checks sit beside the receipt engine rather than silently changing its
contract.
Evidence and contribution boundary
| Type | Contribution here | Evidence |
|---|---|---|
| P: established practice | Calibration uncertainty; reproducibility artifacts; trial registration; legal authority and procedural duties | Primary-source links in each case, checked 2026-09-25 |
| D: protocol design | Small receipts and two entry paths | Explicitly proposed templates |
| T: mathematical illustration | Shared-error floor, nonidentifiability, completion selection, capacity/deadline counterexample | Derivations and executable deterministic oracles |
| I: implementation | Four numerical cases and regression tests | Local command output; no field evidence |
| H: human efficacy | Better scope comprehension at tolerable labor | Unrun comparative study described in each case |
These cases do not claim new statistical theorems. Their contribution is to make well-understood failure modes actionable at the point of reliance. Existing artifacts can already carry much of this information. The closest comparator is a plain structured claim/evidence/limitation template, not unstructured chaos.
No real participants were recruited. Drafting used AI agents; final human authorship and release approval are governed by the release packet, not supplied by these examples. Primary sources contextualize the cases; they do not endorse MCRP.