Redesigning Paper as the Unit of Scholarly Intermediaries

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Redesigning the Paper as the Unit of Scholarly Intermediaries

Abstract

The academic paper was designed as a human readable unit of

knowledge under conditions of print scarcity, slow

dissemination, and journal centered gatekeeping. As AI systems

increasingly generate manuscripts, summarize literatures, and

allocate attention, the paper functions less as a narrative

artifact and more as an intermediary that feeds machine triage,

aggregation, and synthesis. Under these conditions, the binding

constraint shifts from rhetorical persuasion to verification

under abundance. This article argues that the paper should be

redesigned as a structured research object with an evidence

dominant core: explicit claim registries, assumption registries,

machine readable claim to evidence mapping, executable

provenance, and versioning for post publication updating.

Narrative remains important, but primarily as an interpretive

layer that organizes evidence rather than as the principal

carrier of claims. The argument builds on evidence focused

publication systems in applied sciences and on analyses of how

publication systems shape accumulation and impact (Zhang &

Ertug, 2025). The paper concludes with design principles and

governance implications for journals, repositories, and

evaluation systems across disciplines.

1. Introduction

The academic paper is simultaneously a cognitive device and an

institutional object. It structures how researchers make claims,

how communities certify them, and how knowledge accumulates

across time. The dominant format, often operationalized as IMRaD

variants, was engineered for sequential human reading and for

review under scarcity: limited space, limited attention, and

slow diffusion.

Two changes destabilize this fit. First, the marginal cost of

producing scholarly text is declining as AI systems draft,

paraphrase, and elaborate with minimal effort. Second, the

marginal cost of first pass reading is also declining as AI

systems summarize, cluster, and rank papers for humans. These

shifts do not merely increase speed. They change selection

pressure. When text is abundant, the scarce resource is credible

verification.

This article develops a systems claim: the paper is becoming a

unit of scholarly intermediaries. That is, it increasingly

serves as an interface that mediates between (a) human authors

and reviewers, (b) AI systems that triage and synthesize, and

(c) downstream users who treat the literature as an evidentiary

base for decisions, models, and policy. Under these conditions,

narrative first design becomes fragile because it is easy to

generate and hard to audit at scale.

The core proposal is not “less theory.” It is a reallocation of

primacy: evidence and auditability become first class, while

narrative becomes a secondary rendering layer. This orientation

is consistent with how many applied sciences maintain rigor

through structured evidence accumulation and heterogeneous

contribution types rather than requiring each individual paper

to deliver theoretical novelty (Moher et al., 2009; Guyatt et

al., 2008; Vincenti, 1990).

2. The paper as infrastructure, not merely text

Scholars in the history and sociology of science have long

treated scientific outputs as infrastructural artifacts rather

than as neutral containers of ideas. Kuhn (1962) emphasized that

scientific development depends on shared exemplars and

evaluative standards that coordinate a community. Latour (1987)

argued that facts travel through networks of inscriptions and

devices that stabilize claims. The paper is one of the central

inscriptions that allows claims to travel, be contested, and be

institutionalized.

In a human centric regime, the paper’s infrastructural role was

closely tied to narrative competence: motivations, novelty

claims, and discursive positioning. In a machine mediated

regime, the infrastructural role shifts toward structured

interoperability. AI systems that summarize, embed, and retrieve

papers require stable identifiers for constructs, claims,

methods, and evidence. Without such structure, the paper remains

readable but becomes computationally lossy.

This perspective aligns with the view that publication systems

shape what gets produced and how it accumulates. Zhang and Ertug

(2025) frame publication systems as interdependent elements that

affect balance between evidence and theory, diversity of

research types, responsiveness to real world challenges, and the

coupling of exploration and replication. While their argument is

grounded in organization research, the system logic generalizes:

when evaluation systems reward narrative performance and novelty

signaling, the literature becomes theoretically prolific but

evidentially thin; when systems reward structured evidence and

replication, theory can emerge from accumulation.

3. Abundance and the verification bottleneck

A central implication of AI text generation is that plausibility

is no longer a scarce output. If many papers can be produced

that look methodologically orthodox and rhetorically competent,

then surface signals lose discriminating power. The problem is

not that AI generated text is inherently unreliable. The problem

is that volume breaks the current audit regime.

This is an instance of what Borgman (2015) describes in data

intensive scholarship: as the scale and heterogeneity of outputs

increase, infrastructures for curation, provenance, and reuse

become central to credibility. Nielsen (2011) similarly

emphasizes that networked science changes the division of labor,

shifting value toward tools that make knowledge discoverable,

recombinable, and verifiable at scale.

In empirical fields, the reproducibility movement can be

interpreted as an early signal of this bottleneck. The Open

Science Collaboration (2015) demonstrated that even in a human

authored regime, many published findings do not replicate

cleanly. When we add massive growth in output and machine

mediated diffusion, the cost of false positives and overstated

generalizations rises. The question becomes: what design of the

paper makes verification cheaper?

4. The paper as a structured research object

A research object is not a PDF. It is a set of linked, versioned

components that allow claims to be queried, evaluated, and

recombined. The argument here is that the “paper” should evolve

into a canonical rendering of such an object, rather than being

the object itself.

A minimal evidence dominant architecture has four layers.

4.1 Claim layer: explicit claim registry

The primary failure mode of narrative first papers is that

claims are entangled with motivation, literature positioning,

and hedging. Machines and humans both struggle to identify what

is actually asserted. The claim layer makes this explicit.

A claim registry should include, at minimum: (a) the focal

claim(s), (b) whether each claim is descriptive, predictive,

causal, or design oriented, (c) the implied scope conditions,

and (d) the intended level of generalization. In causal work,

this maps to explicit estimands (Shaver, 2020). In theory work,

it maps to explicit construct definitions, proposed mechanisms,

and discriminating predictions.

4.2 Assumption layer: explicit assumption registry

Claims are only as credible as their assumptions. Yet

assumptions are often implicit, scattered, or framed as

limitations rather than as formal conditions for inference.

Making assumptions explicit is central to machine mediated

accumulation because it enables downstream systems to compare

claims under comparable conditions.

For causal inference, this includes identification assumptions,

measurement assumptions, and model dependence. For observational

research, reporting standards such as STROBE exist precisely to

increase transparency and comparability (von Elm et al., 2007).

For systematic synthesis, PRISMA institutionalizes explicit

reporting of inclusion, exclusion, and bias considerations

(Moher et al., 2009). These standards are not rhetorical. They

are designed to reduce ambiguity in cumulative evidence systems.

4.3 Evidence layer: claim to evidence mapping with uncertainty

An evidence dominant paper binds each major claim to the outputs

that support it and makes uncertainty a first class object. In

many empirical papers, uncertainty is present but not mapped to

claims. The result is selective emphasis: readers infer

confidence from rhetoric rather than from structured evidence.

The health sciences offer a mature model of evidence grading, in

which strength of recommendation is separated from quality of

evidence, often using frameworks such as GRADE (Guyatt et al.,

2008). The key design principle is separability: claims can be

strong, tentative, or speculative, but the evidence basis is

explicitly graded.

In a machine mediated regime, this mapping enables automated

meta inference: aggregation of effect sizes, detection of

heterogeneity, and identification of boundary conditions. It

also enables rapid human audit: a reviewer can interrogate the

evidence object directly rather than reconstructing it from

narrative.

4.4 Provenance and executability layer: data, code, and lineage

Evidence dominance requires that the analysis pipeline is

inspectable. This is now routine in many computationally

intensive fields. In medicine, machine learning systems

increasingly require careful reporting of training data,

evaluation, and drift (Rajkomar et al., 2019). In climate and

Earth system science, deep learning is integrated with process

understanding, but credibility depends on data provenance and

model validation (Reichstein et al., 2019).

For the paper, the implication is straightforward: data schemas,

preprocessing, and code should be treated as integral components

of the research object, not as optional supplements. This is not

only about replication. It is about enabling machine systems to

identify comparable constructs and to reuse evidence without

reinterpreting prose.

5. Where does theory sit in an evidence dominant design

A common objection is that evidence dominance risks narrowing

scholarship to what is measurable and executable, marginalizing

conceptual innovation. This concern is real, but it misstates

the tradeoff.

In evidence dominant design, theory remains essential as an

organizing schema. Simon (1996) characterized design as the

science of the artificial, emphasizing that complex human made

systems require iterative cycles of problem formulation,

artifact creation, and evaluation. A parallel point applies

here: theory is the schema that organizes what counts as

evidence, which contrasts are meaningful, and what boundary

conditions are plausible.

The key shift is functional. Narrative theory work becomes more

valuable when it produces structure: precise constructs,

explicit mechanisms, and discriminating predictions that can be

linked to evidence objects. In other words, theory moves from

rhetorical positioning toward schema design for accumulation.

This view is consistent with how engineering knowledge develops

through epistemic accumulation rather than isolated theoretical

leaps (Vincenti, 1990). It is also consistent with calls in

strategy and organization research to emphasize cumulative

identification over single study claims (Shaver, 2020) and to

treat replication as a core component of knowledge development

(Bettis et al., 2016).

6. Implications for journals, repositories, and evaluation

systems

If papers become research objects with structured cores, then

journals’ comparative advantage changes. In a scarcity regime,

journals curate narrative contributions and allocate prestige.

In an abundance regime, journals can differentiate by certifying

auditability: completeness of claim registries, explicitness of

assumptions, quality of evidence mapping, and executability.

This logic parallels Zhang and Ertug’s (2025) argument that

publication systems differ in how they balance evidence and

theory and how they enable exploration and replication. Under

evidence dominance, exploration becomes easier to publish

because exploratory claims can be explicitly labeled and

bounded, and because replication has a defined place in the

system rather than being treated as a second class contribution

(Bettis et al., 2016). Responsiveness can also improve because

evidence objects can be disseminated quickly, with subsequent

versioned updating.

For applied fields, relevance becomes less about rhetorical

appeals and more about portability of evidence across contexts.

Entrepreneurship research, for example, faces an ongoing

relevance challenge (Wiklund et al., 2019). Evidence dominant

objects can support relevance by making effect sizes,

heterogeneity, and boundary conditions explicit, enabling

practitioners and policymakers to judge applicability without

relying on narrative translation.

7. Governance risks: formalization, inequality, and gaming

Redesign is not merely technical. It has governance risks.

First, formalization can become performative. Authors may “fill

fields” without increasing transparency. This risk is minimized

when fields are linked to executable artifacts and when audit

trails exist.

Second, evidence dominance can increase inequality if

infrastructure access determines credibility. This mirrors

concerns in data intensive scholarship (Borgman, 2015).

Governance responses include shared repositories, standardized

tooling, and journal level support for reproducibility checks.

Third, structured systems can be gamed. Any metricized field is

vulnerable to optimization. The remedy is plural certification:

multiple signals tied to replication, calibration, and post

publication performance rather than to single score proxies.

Improvement science offers a pragmatic analogy: building

reliable systems requires iterative feedback loops and attention

to system design, not only to individual performance (Bryk et

al., 2015). Applying that logic here implies that publication

reform should be treated as system engineering.

8. Conclusion

The paper is evolving from a narrative artifact into a unit of

scholarly intermediaries that feeds machine triage, synthesis,

and downstream decision systems. Under AI mediated abundance,

the scarce resource is verification. The design response is to

move toward an evidence dominant research object architecture:

explicit claim registries, explicit assumption registries,

structured claim to evidence mapping, and executable provenance,

with narrative serving as an interpretive rendering layer.

The argument is compatible with the broader idea that

publication systems shape accumulation and impact, and that

evidence centric designs can enable more responsive, diverse,

and cumulative science (Zhang & Ertug, 2025). The generalization

is that this logic now applies to scholarship as a whole, not

only to any one discipline, because AI systems increasingly

intermediate both production and attention.

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