Case packet
Neutral case identifier: case-013bbf561b7940
The possible directed relationship from Artifact A to Artifact B is under review.
Artifact A
Methodological and computational closure track different changes
An improvement pathway can stop depending on improvised judgment without stopping its dependence on a human actor, and it can stop depending on a human actor while continuing to improvise. Those are different architectural changes and need different readings of closure.
Methodological closure asks whether the retained methodology settles the consequential decisions that the pathway raises. A method is less closed where it merely says “use judgment,” names an approver, or leaves a meta-decision to be reconstructed from scratch.
Computational closure asks who supplies the decision. A function is computationally closed when its execution needs no human decision; a whole pathway is computationally closed only when every required function meets that condition.
Computational closure and machine autonomy therefore read the same actor allocation: human, computational, or joint for each pathway function. “More computationally autonomous” describes movement in that allocation; “more computationally closed” describes the resulting reduction in functions that still require a human decision.
Neither reading is the cybernetic sense. Organizational closure — the recursive regeneration of a network of component interactions in the autopoiesis tradition — is a different property, already excluded from this cluster's vocabulary in the [reflective-system exclusions]; nothing here asserts or requires it.
Human-inclusive boundaries make allocation load-bearing
A [reflective system] may include established human processes. Put a maintainer with a standing causal role inside the boundary of a maintained system with readable source, and reflective attribution becomes cheap: the maintainer inspects the source as a representation, edits it, and the build carries the edit into operation. The attribution can be true while saying little about machine performance.
Actor allocation restores the missing discrimination. Under a fixed human-inclusive boundary, report each consequential function as human, computational, or joint; computational closure is the no-human endpoint of that profile. Do not replace the profile with a percentage: functions differ in decomposition, authority, and stakes, and cross-system comparison remains [an open measurement problem].
The form is inherited rather than invented. [Parasuraman, Sheridan, and Wickens] report automation per function — information acquisition, analysis, decision and action selection, action implementation — and hold that an allocation is judged by its performance consequences, its reliability, and the cost of the consequences it admits, not by how much of the work the machine has taken over. That shape is what carries across, with three departures. The functions allocated here are the improvement pathway's own — search, evaluation, and retention where the pathway is proposal-selection — rather than task-performance stages. Their within-function ten-level scale is not inherited: the paper's validation is strongest for decision selection, and a graded level per function would reintroduce the percentage this profile refuses. And allocation still establishes nothing about warrant.
Four concrete combinations
| Improvement decision | Methodologically closed? | Computationally closed? | Why |
|---|---|---|---|
| A maintainer manually applies an exact checklist before accepting a patch | Yes | No | The criterion is settled, but a human supplies the verdict. |
| A validator accepts an artifact only when an exact structural predicate holds | Yes | Yes | The criterion and its execution are both explicit and computational. |
| An unattended coding agent is told to inspect failures and “improve the repository” using its own judgment | No | Yes | No human intervenes, but consequential choices remain improvised. |
| A maintainer and agent jointly judge a theory note against “is this good?” | No | No | The criterion is unsettled and a human participates in the verdict. |
Stable but tacit expertise does not count as retained methodology. A maintainer may apply a repeatable internal criterion that was never externalized — settled in practice, unsettled in representation — but methodological closure reads the representation, and the reading has one ground rather than a human-specific rule, [since only explicit retention is currently durable, writable, and addressable at once]: a criterion that cannot be retrieved, cited, criticized, or selectively revised is not available to the pathway as methodology, however consistently it is applied — it is available only as the human actor. The state deserves its own name instead of a closure grade: stable-but-unexternalized practice is a promotion candidate, noticeable by recurrence and convertible by externalization. The last row therefore stands even when the joint judgment is secretly consistent.
The third row needs a named exclusion, not a stronger definition. Computational closure reads actor allocation within the declared frame: a hosted model is a computational actor wherever it runs, so a pathway can be computationally closed while depending on inference infrastructure and a provider outside the selected subsystem. That dependency is real, but it is a boundary and coverage fact — in profile terms, selection-grade coverage of a sealed parametric component, [as reflective coverage is graded across representational forms] — not an actor fact. Widening closure to swallow substrate dependency would leave almost no model-mediated function ever computationally closed and destroy the discrimination the table exists to provide, the same reason the organizational-closure sense is excluded above.
When the two changes advance together
A recurring human decision becomes easier to allocate computationally after its inputs, criterion, and failure response have been made explicit. The conversion usually has three parts:
- Representation — the relevant inputs and commitments become available to the deciding process, [since reflection buys addressability].
- Settlement — the methodology supplies the criterion or determines the result instead of merely naming a decider, [since a methodology governs its own extension only as far as it settles the meta-decisions it raises].
- Warranted execution — a computational procedure or oracle implements the criterion with evidence adequate to the case, [since warranted autonomy is bounded by oracle domain].
The order is forced, not conventional: externalization is allocation's transport. A computational actor can receive a criterion only through an explicit representation — under a selection-only parametric profile nothing else inside the boundary is both writable and durable, and even where fine-tuning adds a write channel the transfer is unaddressable, escaping governance at the moment it succeeds ([only explicit retention is currently durable, writable, and addressable at once]).
These are engineering dependencies, not definitions of one another. A settled gate can remain human-executed; an agent can read explicit commitments yet improvise how to apply them; and a computational procedure can encode a poor proxy. Moving evaluation to a model changes allocation without establishing that its acceptances are trustworthy.
The [Commonplace reference case] applies this conversion to ADR 026 and keeps the trace-specific facts in one place.
Reflection is a separate question
Reflectivity does not require methodological closure. It requires a causally connected representation of the system's own behavior that processes inside the declared frame can read and change. A reflective pathway may expose its rules for criticism while leaving the next revision to open-ended judgment. Conversely, a fixed pipeline may settle every operational choice without representing or revising itself.
The properties reinforce each other when the represented object is the improvement methodology itself: an addressable criterion can be revised, then a settled and warranted version can be executed computationally. That is a trajectory through a [multi-part profile], not one scale of reflectivity or closure.
Scope
- Both closure readings are per decision and per pathway, so mixed profiles are normal: exact validators can coexist with joint review, and settled acceptance rules with improvised objective-setting.
- A loop instance completes when search, evaluation, and operative retention occur. Calling that event closure would conflate completion with architecture.
- Both readings require a declared frame. A whole-system closure claim without named decisions and pathways hides the mixed architecture.
- Comparing allocation profiles across releases or systems inherits the open commensurability problem: [measuring autonomy well enough to see it improve is an open problem].
Open Questions
- When an initial human instruction makes a downstream agent-performed function joint rather than computational; counting every instruction hides agent performance, while counting none hides decision content supplied up front.
- Whether objective-setting can become methodologically closed without freezing the improvement objective rather than improving it.
- How much representational explicitness computational internalization requires when learned components can execute a decision without exposing its criterion.
- How to distinguish a computational implementation of a settled method from a proxy that silently changes what the method decides.
Relevant Notes:
Artifact B
Only explicit retention is currently durable, writable, and addressable at once
A system that learns during operation must retain what it learned in some form, and the candidate forms differ on three properties that decide whether the retention can be governed. Durability: the retention survives the session or run that produced it. Writability: the system's own operation can change it. Addressability: processes inside the boundary can treat a retained commitment as an object — retrieve it, say what it claims, criticize it, revise it selectively, carry it to a new problem — since [reflection buys addressability]. Cutting across all three is the tacit/explicit divide of [representational form]: explicit forms (natural-language, symbolic) encode commitments readably; tacit forms (distributed-parametric state, conditioned context state, embodied human expertise) encode competence that cannot be read out as commitments.
| Retention form | Durable | Writable | Addressable |
|---|---|---|---|
| In-context conditioning | no — dies with the session | yes — every token writes it | no — the transcript is addressable; the competence it induces is not |
| Weights, selection-only profile | yes | no — swapping the sealed component is the only lever | no |
| Weights, with fine-tuning | yes | yes | no — no per-commitment retrieval, criticism, selective revision, or rollback |
| Human expertise | yes | yes — practice writes it | no — stable, perhaps, but not inspectable, diffable, or transferable |
| Explicit artifacts (natural-language, symbolic) | yes | yes | yes |
Addressability here is comparative, not absolute — the reflection note is explicit that opaque retention still admits indirect handles (behavioral probing, wholesale rollback, retraining, steering). The column records whether any handle operates on the retained commitment as an object, and for every tacit row the answer is no.
Two rows deserve unpacking. In-context conditioning looks explicit because its medium is text. But the readable transcript and the induced competence are different objects: no sentence of the transcript contains the calibration the whole context conditions into the model, so the competence cannot be excerpted, revised, or transferred sentence-wise — the transcript is the explicit trace of a tacit state. Fine-tuned weights and human expertise fail the same property, and the symmetry is the point: both are durable, writable (by training, by practice), and rich, and in neither can a single retained commitment be diffed, cited, criticized, or rolled back. Verification collapses to behavioral probing in both cases, exactly as the per-form review obligation in [reflective coverage] predicts for the parametric form. So when [methodological closure] declines to count a maintainer's tacit-but-stable criterion as retained methodology, and when a knowledge system declines to retain lessons by fine-tuning, they apply one criterion, not two special rules: closure and governance read the addressable channel.
Externalization is the transport, not a preliminary
The conversion of a human-held decision to computational execution puts settlement — making the criterion explicit — before allocation. The table shows that ordering is forced, twice over. In a system whose [operation profile] over the parametric form is selection-only, explicit artifacts are the only durable writable channel there is: nothing else inside the boundary can receive the criterion at all. And even where fine-tuning adds parametric writability, the transfer is unaddressable — what was allocated can no longer be stated, reviewed, or revised as a commitment, so the allocation escapes governance at the moment it succeeds. Either way, a criterion moves to a computational actor only through an explicit representation. Externalization is the transport mechanism of allocation, not preparation for it.
The claim is current, and its falsifier is named
This is an empirical claim about existing substrates, so its application to a particular system should be stated as a profile line over the parametric form, not as a fact about technology at large — [Commonplace as a reflective system] records a selection-only line, and everything this note forces (explicit-only retention, externalization as transport) is downstream of that line. What would break the claim is not more writability: fine-tuning exists now, and continual-learning pipelines only add write channels. The falsifier is addressability of a tacit form — interpretability-grade editing under which a weight-encoded commitment can be individually retrieved, criticized, and revised. A system whose profile line crosses that boundary owes every artifact derived from this claim a re-derivation.
Scope
- Not a ranking. Tacit forms hold what explicit ones cannot: the residue — calibration, situational feel, style — that resists articulation and is often the competence that matters. The trade runs both ways, and the consequences for what to retain in which form are developed in [retaining the episode keeps a distilled rule re-derivable].
- Retention through an actor the system neither observes nor selects — a provider training on usage data — is excluded: whatever returns through later model versions is not a retention pathway of the system, and no property in the table applies to it.
Open Questions
- Whether behavioral probing, evals, and activation steering constitute a graded middle — partial addressability worth naming — or stay indirect handles in kind.
- Host binding: the tacit-knowing literature (Polanyi's "we know more than we can tell"; Nonaka's externalization step) is the natural host for the tacit/explicit side of this claim. Inheritance is deferred until the sources are ingested and a canonical statement chosen.
- Whether the conditioned-state/transcript split recurs often enough to need a registered term.
Relevant Notes:
Under-review context phrase
the shared reason tacit expertise is not retained methodology and externalization is allocation's transport