Superseded 2026-08-21 This is an earlier version of the Civic AI Commons page, kept for its proposal table and measurement detail. Several claims on this page are known to be false and were corrected on the current page: it says four confirmed transfers and "verified once" (the count is five, restated down from six), it publishes a figure of 177 cross-domain candidate pairs (that figure is not supportable, because the field it is computed from records which literature a paper was studied in rather than where a mechanism belongs), and it states that the tag vocabulary is enforced when a record is written (it is not). Read it as a record of what was claimed, not as current documentation. Go to the current page
Civic AI Commons / Democratize The Internet

Cross-domain transfer · verified once, proposed 61 times

The answer already exists. It is filed under someone else’s problem.

Not because researchers are hiding anything. Because finding it means looking in a field that is not yours. Every line below joins two interventions from different domains that break the same institutional failure by the same mechanism. One of these has been verified against the literature. The rest are candidates.

Why this exists

People are good at analogies. They are bad at finding them.

In 1980, psychologists gave students a hard problem: destroy a tumour with radiation without burning the healthy tissue around it. About one in ten solved it.

A second group read a story first. A general attacks a fortress by splitting his army into small groups that converge from different roads. Same problem, different nouns. Solving rates roughly tripled.

A third group was told to use the story. Three quarters solved it.

The gap between the second and third groups is the whole finding. People who had just read the answer mostly did not think to apply it. What failed was not reasoning. It was noticing that a relevant case existed.

Gick & Holyoak, Cognitive Psychology, 1980 and 1983.

What we found looking at real cases

Transfers happen when one person happens to know two fields.

We took five documented cases where a solution moved from one domain to another and looked at how each one actually happened.

Cure Violence. Gary Slutkin spent a decade running epidemic response for the WHO, came back to Chicago, and was asked about street violence. He looked at the maps and saw an epidemic curve.

Camden hotspotting. Jeffrey Brenner was a Camden physician who sat on a police reform committee, saw CompStat crime maps, and asked whether health data would look the same. It did.

CitiStat. Martin O’Malley saw CompStat in New York and brought it to Baltimore city agencies. He did not just copy the idea. He hired Jack Maple, one of its creators, as a consultant.

Forensic ACT. A psychiatrist treating patients who cycled through jail knew both the clinical model and the courts.

Four of five needed one person carrying two fields in their own head. The fifth is the one that failed: Mexico’s conditional cash transfer programme was studied carefully and brought to New York in 2007 by a policy team scanning international evidence. No carrier. It underperformed.

So the current mechanism for connecting fields is biographical accident. A transfer waits for someone to happen to have had the right career. That is what a database can replace. Not insight. Coverage.

The verified case

Two records the system had never been told were related.

Each record in the corpus carries two structural tags: the mechanism it uses, and the institutional failure it corrects. Those tags are assigned from the source paper, one record at a time, with no knowledge of any other record. The match below was computed from the tags alone. It ranked second out of 1,948 candidate pairs.

What would break this transfer

Practitioners built this fusion in 2004.

Forensic Assertive Community Treatment delivers ACT under court mandate to people cycling between psychiatric crisis and incarceration. It resolves the difference above by design: the clinical team is continuous, the court supervision is time-limited and conditional.

The system was not told this. It matched two tagged records and produced the same pairing, along with the objection a practitioner would raise.

What we did not expect. These two are not far apart. Once you count every field each one belongs to, they overlap on three of four, which puts them among the most closely related pairs in the corpus. That suggests something worth testing: transfers may work because the fields are adjacent. Far enough apart that nobody looked. Close enough that the model survives the move. We have one confirmed case, so that is a hypothesis and not a finding.

Where this stands

Four confirmed transfers, and one the field reached first.

Twelve problem-to-solution candidates the system proposed were checked against published literature. Four are documented transfers with the lineage stated in the sources. Three of those four also address the problem they were matched to.

Baltimore’s CitiStat took CompStat from the New York police department into municipal services in 1999. The mayor hired the man who built CompStat as a consultant. Performance management moved from public administration into schools, and motivational interviewing moved from addiction counselling into managing high blood pressure. In each case the source literature names where the method came from.

Two of those three are among the best-documented cases in their fields. That matters: it shows the system can surface a real transfer without being told about it, and it does not yet show the system can surface one nobody has written up.

The fourth confirmed transfer is participatory budgeting, which moved from Brazilian city government into schools and is documented working. It does not clearly answer the problem it was matched to, so it counts as a real transfer and not as a real match. Of the rest, four were not transfers at all, and three were correct matches inside a single field. We keep all of them, because a pattern in what gets rejected is how the scoring gets corrected.

The candidate we would point at is not on that list. Federal homelessness funding requires agencies in a region to coordinate as a condition of the money. The system matched that against fragmented care for sickle cell disease, where specialists work in isolation. Then we checked, and Congress had funded that same structure in 2004. Nobody told the system that. Arriving independently at something the field already reached is a stronger result than an untried idea would have been, and it is one candidate in twelve.

The open proposal

What would have to be true.

Nobody proposed this pairing. The system matched a homelessness funding rule against fragmented sickle cell care because both describe the same structure: providers who each hold part of a person’s situation and no body that holds the whole of it. Then we checked, and found the pairing was not new. Congress created a Sickle Cell Disease Treatment Demonstration Program in 2004, and since 2006 it has funded regional collaborative networks on exactly this logic. Nobody told the system that. It arrived at a structure the field had already reached.

The mechanism has two halves. Federal homelessness funding requires every agency in a region to feed one shared registry, and it requires them to join one coordinating body. The registry is the cheaper half.

That is the gap the sickle cell literature names. Adults leaving paediatric care face a sevenfold rise in mortality, largely because there are almost no adult providers who specialise in the disease, so patients scatter across primary care and emergency departments. New York City’s health department named the absence of a comprehensive patient registry as a core failure last year. The information exists. It sits in pieces nobody can assemble.

Something has already been tried here. Between 2018 and 2019, North Carolina attempted to push evidence-based sickle cell guidance through the state’s Medicaid managed care network, a network covering roughly ninety percent of the state’s primary care providers, and directly to emergency department staff at a smaller scale. Utilisation did not move: no significant change in specialty visits, emergency visits, or hospitalisations.

The authors say something more useful than the null result. They could not confirm the guidance reached the providers at all. Delivery ran through quality-improvement specialists and the study reports being unable to track whether any given practice received it. So the finding is not that knowledge did not help. It is that nobody could establish the knowledge arrived, which is the same shape as the problem: information exists, and no body holds it or moves it.

That does not test the funding condition, which is a different mechanism from guidance. It sets a bar: whatever is tried next has to be something whose arrival can be verified.

The two approaches came from different places, and that is the part worth noticing. North Carolina’s came from inside the field. When someone who knows the disease sees that providers lack knowledge, distributing knowledge is the obvious move. Ours came from a housing funding rule, which nobody inside haematology would have thought to look at, because there is no reason to.

That discovery turns the question around. It is no longer whether regional coordination would help. It is why the coordination that exists has not moved the outcome, and the answer names what the transfer would actually need.

Scale is the first part. The federal sickle cell treatment programme runs on about $8.2 million a year against $4.04 billion for the homelessness competition. That is roughly eighty-two dollars per patient per year, which cannot compel anything.

But the deeper condition is that coordination needs somewhere to route people. Coordinated entry works in homelessness because permanent supportive housing units exist to assign. Adult sickle cell care has no equivalent, and the reimbursement structure actively prevents one: a dedicated sickle cell unit cut admissions by 43% and likely lost its institution money under how inpatient care is paid for, while oncology procedures pay better than non-malignant blood disorders, so the specialists migrate away. Coordination cannot allocate capacity that does not exist.

What would change that reading is a region where adult specialty capacity demonstrably exists and is underused while outcomes stay poor. That would make routing the binding constraint rather than supply. We have not found one.

What it would mean

The same barrier, in four buildings on the same street.

A housing office turns people away because the paperwork defeats them. A clinic loses patients between referral and first appointment. An election office watches eligible voters fail to register. A retirement plan sits unused because enrolling takes an afternoon.

Four institutions, four literatures, one structure. Each has partial answers the others have never read.

That is not a knowledge problem. Every one of those fields has done the research. It is a retrieval problem, and retrieval is what a corpus is for.

The working version of this is unremarkable to describe. Someone facing a problem writes it in their own words. They see which other fields have the same structure, what each of them tried, what it required, and where it broke. Then they decide, because deciding was never the part that was missing.

This is a draft of the argument, not a description of a finished product. Four transfers have been verified against literature, out of twelve candidates checked. The rest is a method that produces candidates and a corpus too young to have met practice.

How it works

The full measurement, every proposed transfer, the tests the method failed, and how a record is built.

What we measured

The system proposes matches. Four have been confirmed against literature.

Each problem in the corpus is described by the shape of its failure, in one sentence, with the domain nouns stripped out. Each intervention is described by how it acts. Both are compared as meaning rather than as keywords, so two records can match with no tag in common.

That has produced 177 cross-domain candidate pairs in the current benchmark. Twelve were checked against the literature by hand and four were confirmed as real transfers, none of them seeded.

Rows and columns are the field each source paper was studied in. This is the provenance of the papers, not the domain of the mechanisms, so an off-diagonal cell means two literatures met and not that a mechanism crossed a boundary.

What we cannot measure yet. How often a mechanism actually crosses a domain boundary is not something we can currently measure. The category on each record is inherited from the search topic and describes where the paper was studied, not where the mechanism belongs. A hand read of forty records found that distinction wrong in more than half of them when read the second way. We are not publishing a corrected percentage, because the mislabelled records are not a random sample of the ones that surface, which makes any adjusted figure unreliable rather than merely smaller. The matching itself does not use those labels. It compares mechanism descriptions, which is why the four confirmed cases survive the correction. The confirmed count is four, and it was reached by reading papers.

Civic & democracy

Same-day voter registration

Acts by
lowering the administrative and temporal costs of participation, so people who would otherwise be turned away take part
the registration step is the barrier

Healthcare

Community-governed enrollment

Acts by
replacing formal bureaucratic registration with local vouching, so the same barrier is removed

Those two share no tag, no vocabulary, and no literature. They share a move: the registration step is itself the barrier, so remove it or relocate it. Someone running a health enrollment programme would not read election administration research, and the reverse is equally true.

A proposed answer is not a verified one. These are candidates a person still has to check, and most will not survive checking. The number says the method produces cross-field candidates rather than noise. It does not say the candidates are right.

Every proposed transfer

Filter by the mechanism two interventions share.

These are proposals, not findings. Each names two interventions from different domains, the mechanism they share, and where the transfer would run into trouble.

What we got wrong

The categories failed a test we ran on ourselves.

Every intervention here is sorted into one of thirteen mechanism categories. We tested whether that sorting could recover transfers we already knew about.

It could not. Mexico’s conditional cash transfer programme landed in one category. The same programme, transferred to New York, landed in a different one. Both classifications were defensible. The programme has two mechanisms and the field holds one.

So we are replacing the category with a sentence describing how the intervention acts. On the cases we can check, the sentences pair correctly where the categories did not.

We will publish what fails as well as what works. A method that has never been shown to fail has not been tested.

How a record becomes a claim

Source first, tagged once, matched later.

  1. A paper comes first

    Nothing is written from a model's memory. A topic is searched, candidate papers are scored for relevance, and a record is generated only from a source that clears the threshold. Anything below it is skipped and logged.

  2. Tags come from a fixed vocabulary

    Thirteen mechanisms, fifteen institutional failure modes, eleven domains. The vocabulary is enforced when the record is written, not cleaned up afterwards, so two records that share a tag genuinely share it.

  3. Matching happens across records, never inside one

    A pair is proposed when two records share a mechanism, share a failure mode, and sit in different domains. The difference note is generated from where they diverge, because a transfer with no stated objection is advocacy rather than analysis.

  4. A human decides what is true

    Every proposal is unvalidated until someone finds published evidence that the fusion was built. That step is not automated and will not be.

Why this exists

The argument, the one verified case, and what it would mean if the method holds.