1. Why this question matters

A familiar publishing question starts this scenario: What does a reader need in order to inspect, challenge, and reuse the evidence behind an answer? As the Key Image suggests, a scholarly publisher has obligations to authors, readers, libraries, institutions, and the record itself (1, 2). Those obligations do not disappear when discovery begins in an AI assistant instead of on a journal website.

The olive tree provides a useful editorial metaphor because it is associated with peace, friendship, life, longevity, and health. It also rewards patience. A well-tended tree cannot be rushed into maturity; neither can a trustworthy content service. The purpose of the metaphor is not to decorate a technical proposal. It is to keep attention on the human relationship around the record: someone created the work, someone published it under defined terms, someone needs to understand it, and someone must be able to check the route from answer back to evidence.

2. Scenario and method

The workshop compared three answer formats in a simulated literature search: an uncited response, a response linked only to a journal homepage, and a response with DOI, article title, section locator, quotation boundary, and version label. Sixty fictional participants were timed while tracing a claim back to the sentence it came from, across three answer formats. The layered process is shown in Figure 1 (3). The point was to expose decisions a real implementation must make explicitly: what information travels with a retrieved passage, which permissions are checked before delivery, and which uncertainties remain visible rather than being polished away by an elegant interface.

1. Choose a named collection and record its accountable owner.

2. Retain identifiers, rights, version status, source links, and accessibility information.

3. Apply a purpose-specific entitlement before returning a passage.

4. Return a source-linked answer with an access-aware next step.

5. Report use with definitions and limits that a publisher can explain.

3. What the scenario revealed

What emerged was straightforward: the locator-bearing format supported the fastest verification and the clearest handoff to formal reading. Participants described it as less magical, which the authors regard as a compliment in scholarly communication. Verification time dropped from just over four minutes to under forty seconds once a locator was attached, which one participant summarised as ‘oh, it’s just there.’ (4, 5). It suggests that the quality of an AI experience depends less on producing an effortless sentence than on preserving the context that lets a reader judge that sentence. Source links, identifiable versions, clear licences, accessible descriptions, and meaningful locators are not housekeeping.

In this mnemonic, T denotes a trustworthy response; S, a resolvable source; V, visible version context; A, authorised access; and E, interpretable usage evidence. It is an editorial model, not a quantitative claim. The minimum expected signals are listed in Table 1.

“A response becomes more trustworthy when a reader can see both what it knows and where its authority stops.”

4. The hard boundary

The practical tension is equally important: Citations must not be decorative. They should connect a claim to a real source in a way that remains meaningful when interfaces, platforms, and link structures change. As Equation 1 suggests, the record should therefore make a distinction between information held by the publisher, permissions granted for a purpose, content actually retrieved for an approved audience, and a downstream model’s separate training terms. Conflating those layers may be convenient in a presentation, but it is unsafe in a production workflow.

The proposed operating pattern has six steps, summarised alongside the questions each one must answer in Table 2 (6). First, select a collection with a clear purpose and an accountable owner. Second, map identifiers, rights statements, source links, accessibility information, and version relationships. Third, apply an entitlement decision before retrieval. Fourth, return only the passage or metadata the policy allows. Fifth, cite the source and make its status visible. Sixth, record enough usage evidence to learn whether the service is helping, while being candid about the limits of what the data means.

5. Implications for publishers

These steps leave substantial editorial work where it belongs: with the people who understand the content and its community (7, 8). They do not replace publishing platforms, production systems, library relationships, or customer relationships. They create a governed layer through which trusted material can become useful in more places without becoming untraceable. That is an intentionally modest claim, but it is a useful one.

Scholar Core demonstrates one side of this work: structured JATS ingest, validation, accessibility audit, semantic rendering, versioned lifecycle, search, and a reader-facing record. Razeta demonstrates the adjacent question: how selected collections can be made useful in approved AI workflows while a publisher retains control of rights, access, attribution, and usage visibility. Neither promise eliminates editorial judgement; each makes that judgement easier to express in a durable system.

6. Closing reflection

Nobody in the study asked for a more exciting citation. They asked for one that worked, which turned out to be the exciting part.

A reader should be able to follow an answer back to a source, understand whether the record has changed, recognise the boundary of permitted use, and find an accessible path to the underlying work. That is the serious purpose beneath this issue’s olive-tree humour.