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1. Introduction

The encyclopedia is one of the oldest and most important formats for organizing and presenting human knowledge. Unlike texts intended to express personal opinion or to advance novel argument, encyclopedic text rests on the representation of information already published in reliable sources. This feature places the encyclopedia writer before a twofold problem: on the one hand, information scattered across various sources must be selected, combined, and organized; on the other hand, in doing so, the boundary between representing information and producing independent judgment must not be erased.

In collaborative encyclopedias, this problem takes on new dimensions. On Wikipedia, neutrality (Neutral Point of View), together with verifiability and the prohibition of original research, form three foundational content policies that jointly determine what content is acceptable for the encyclopedia . Under this policy, encyclopedic content must represent all notable viewpoints published in reliable sources "fairly, proportionately, and, as far as possible, without editorial bias" . In practice, however, this principle is often reduced to a superficial rule: it is assumed that the presence of a citation at the end of a sentence, or the use of phrases such as "some researchers believe," is sufficient to achieve neutrality. Such a view reduces a broader problem to a narrow linguistic technique.

This paper aims to redefine neutrality not as a purely linguistic or policy-based property, but as the product of a multi-stage chain of editorial decisions—a chain that begins with source selection and culminates in the final form of the proposition in the encyclopedic text.

2. Research Problem

The existing literature on neutrality in collaborative encyclopedias can be grouped into four general categories: normative studies of content policy, computational research on the measurement and detection of textual bias, comparative studies of cultural and linguistic differences across language editions of the encyclopedia, and studies of the social dynamics of collaborative editing. What has received comparatively less attention in this literature is the examination of neutrality as a property that is built up—or eroded—stage by stage over the course of the content-production process.

Much of the bias-detection research examines the final text at a fixed point in time and treats bias as a property at the level of the word or sentence. Encyclopedic policies, too, largely state neutrality in the form of general principles, without disaggregating the operational stages that transform a source into an encyclopedic proposition. As a result, a question that has received less of an answer is this: at which points of editorial decision-making is neutrality built or violated, and how can these points be formulated in a way that is both teachable to editors and auditable, whether automatically or semi-automatically?

The research problem addressed here is how encyclopedic neutrality can be formulated as a chain of process components that both clarifies the distinction between neutrality and equal representation, and makes it possible to trace and audit each proposition back to its source and to the editorial decision that constructed it.

3. Literature Review

3.1. Normative Policies on Neutrality

Wikipedia's Neutral Point of View policy stipulates that all notable viewpoints published in reliable sources must be represented in proportion to their standing . This policy, together with the verifiability policy, which emphasizes that content must be attributable to reliable sources , and the no-original-research policy, which prohibits any combination or analysis that reaches a conclusion beyond what the sources state , form the three principal pillars of the normative framework of collaborative encyclopedias. While these policies clarify general principles, they do not specify the operational steps by which a source is turned into a proposition.

3.2. Measurement and Detection of Textual Bias

Computational research has sought to identify bias at the linguistic level. Recasens, Danescu-Niculescu-Mizil, and Jurafsky, by analyzing real Wikipedia edits made to remove bias, distinguished two categories of bias: "framing bias," related to the use of laudatory or partisan vocabulary, and "epistemological bias," related to presupposing or taking for granted propositions that are contested ^[1]. Greenstein and Zhu, through a computational analysis of Wikipedia content, showed that textual bias remains measurable even in an encyclopedia with a formal neutrality policy ^[2][3]. Matei and Dobrescu have likewise argued that the Neutral Point of View policy, in practice, serves a dual function: as a normative writing principle, and as a mechanism for suppressing editorial disputes through controlled ambiguity ^[4].

3.3. Cultural and Linguistic Differences

Comparative studies have shown that neutrality can be realized differently across the various language editions of an encyclopedia. Callahan and Herring, by comparing biographical articles in the English and Polish editions of Wikipedia, showed systematic differences in the extent of coverage, detail, and tone of articles, shaped by the history, culture, and values of each language community—differences that cannot be detected by sentence-level linguistic criteria alone ^[1]. These findings show that neutrality cannot be assessed solely at the level of the wording of a single article; it must also be examined at the level of source selection, topic coverage, and information omission.

3.4. Social Dynamics of Collaborative Editing

Kittur and Kraut showed that the quality of Wikipedia articles is a function of coordination mechanisms among editors; adding more editors improves quality only when accompanied by adequate coordination ^[5]. Kittur, Suh, Chi, and Pendleton likewise examined the dynamics of conflict and coordination in the collaborative editing process and showed that conflict among editors is an inherent part of the content-production process, not necessarily a sign of a failure of neutrality ^[6]. This line of research treats neutrality as the product of a social process but pays less attention to decomposing that process into specific epistemic stages.

3.5. Research Gap

Taken together, the existing literature has examined neutrality from four distinct angles, but has rarely attempted to combine these angles into a single, integrated process model capable of speaking both to the ordinary editor and to computational auditing tools. The present paper seeks to propose such a model.

4. Methodology

This research was conducted using a conceptual-theoretical method. The proposed framework is derived from the synthesis of four categories of sources: first, the text of the content policies of collaborative encyclopedias, particularly the Neutral Point of View, Verifiability, and No Original Research policies ; second, the computational literature on textual bias detection ^[1][3][4]; third, the linguistic literature on academic attribution and evaluative language, particularly Hyland's concept of academic attribution ^[7] and Martin and White's Appraisal framework ^[8]; and fourth, the literature on the social dynamics of collaborative editing and cultural differences among language editions ^[1][5][6].

On the basis of the analysis and synthesis of these sources, encyclopedic content production is formulated as a chain of successive transformations, in which each stage is a potential point of entry or removal for bias. This chain is then extended with two complementary concepts: representational proportionality and neutrality auditability.

5. Engineering Neutrality: The Chain of Encyclopedic Transformations

The framework proposed in this paper treats encyclopedic content production as a chain of ten epistemic and editorial stages: (1) source selection, (2) proposition extraction, (3) viewpoint identification, (4) attribution, (5) rewriting, (6) framing, (7) control of evaluative language, (8) control of inference, (9) coverage and omission, and (10) representational proportionality. At each of these stages, an editorial decision is made that can either strengthen or weaken the neutrality of the final text. Contrary to the common view that treats neutrality as a one-time property residing in the final sentence, this framework treats neutrality as a cumulative property that is built up over the course of the chain and can be examined and corrected at every point.

6. Source Selection

The first decision point is the selection of the sources on which the article is based. Source selection determines the input to all subsequent stages; bias introduced at this stage is therefore reproduced throughout the chain. Three dimensions matter here: the reliability of the source, the diversity of sources in terms of viewpoint, and the diversity of sources in terms of language and geography. Callahan and Herring's research showed that when the sources available to an editor are largely confined to a single language or a single journalistic tradition, even scrupulous adherence to the neutrality policy cannot compensate for systematic differences in topic coverage ^[1]. Engineering neutrality at this stage therefore requires awareness of the scope and limitations of the available body of sources, not merely compliance with reliability criteria at the level of each individual source.

7. Proposition Extraction

At this stage, the editor must determine exactly what the source states: which part of the source's content is factual (fact), which part is the source author's own evaluation, and which part is merely an impression drawn from a general reading of the text. A common error at this stage is that the editor fails to distinguish between what the source explicitly states and what the editor infers from the source. Proposition extraction is, in effect, a precondition for all subsequent stages of the chain; if the proposition is incorrectly extracted from the outset, none of the subsequent measures—including attribution or control of evaluative language—can compensate for the error.

8. Viewpoint Identification

After a proposition has been extracted, it must be determined which category it belongs to: a fact on which there is consensus among reliable sources, or a viewpoint that only some of the sources put forward. The Neutral Point of View policy formulates this distinction through the concept of "notable viewpoints": a viewpoint that is put forward to a notable degree in reliable sources must be reflected in the text, even if it does not accord with the majority view . Incorrect viewpoint identification produces two kinds of error: first, presenting a partial viewpoint as scientific or historical consensus; and second, omitting a viewpoint that, despite being a minority position, has a documented standing in reliable sources.

9. Attribution

Attribution—that is, explicitly linking a viewpoint or evaluation to its holder—is one of the most important technical mechanisms for preserving neutrality. The linguistic literature on academic writing has shown that attribution does not serve a purely documentary function but also plays a role in constructing disciplinary knowledge and in calibrating the author's distance from a proposition; Hyland has shown that academic writers, through their citation practices, participate in the construction of knowledge rather than merely reporting it ^[7]. In encyclopedic text as well, a sentence such as "some historians consider event X a turning point" differs fundamentally, at the epistemic level, from the sentence "event X was a turning point," even if both sentences ultimately cite the same single source. Engineering neutrality at this stage requires that attribution be used not as a decorative linguistic formula, but as a precise marker of the boundary between the source's proposition and the encyclopedia's own statement.

10. Rewriting and Framing

In the process of rewriting, the source's content is rendered into encyclopedic language. This is the stage at which what Recasens and colleagues termed "framing bias" occurs: the use of laudatory, derogatory, or viewpoint-driven vocabulary that renders the author's position ostensibly neutral but, in practice, directional ^[1]. For example, describing a policy as "reformist" versus describing the same policy as "controversial" may both cite the same single source, yet convey a different semantic charge to the reader. Framing also includes the order in which information is presented and the choice of which aspect of a topic appears at the beginning versus the end of the article—an ordering that, without changing the actual content, can shift the relative importance of viewpoints in the reader's mind.

11. Control of Evaluative Language

Evaluative language refers to the set of adjectives, adverbs, and reporting verbs that indicate the degree of the writer's commitment to a proposition or evaluation. The Appraisal framework developed by Martin and White provides a systematic tool for analyzing this kind of language and shows that evaluative vocabulary, although seemingly neutral, can reveal the writer's stance toward a source or a viewpoint ^[8]. In neutrality engineering, control of evaluative language means identifying and moderating vocabulary that converts the source's own evaluation into the independent judgment of the encyclopedia writer, rather than a representation of it.

12. Control of Inference

Control of inference means avoiding the production of a conclusion that no single source states, but that emerges from juxtaposing several documented propositions. The no-original-research policy prohibits this kind of combination, referred to as "synthesis," because even if each individual proposition is documented, the conclusion drawn from combining them may be a conclusion that appears in no source at all . This type of bias is subtler than lexical bias, since it is not detectable at the sentence level and becomes apparent only by comparing the overall structure of the text against the actual content of the sources.

13. Coverage and Omission

Neutrality cannot be assessed solely at the level of what appears in the text; what has been omitted from the text plays an equally important role in shaping neutrality or bias. The systematic omission of one aspect of a topic, even without stating any biased proposition, can present an incomplete or one-sided picture of the topic. Comparative studies have shown that differences in the extent of topic coverage across different language editions of an encyclopedia are themselves a form of indirect bias that cannot be traced at the lexical level ^[1]. Engineering neutrality at this stage requires an explicit question of the editor: has an aspect of the topic that is documented in reliable sources been omitted from the text without justified reason?

14. Representational Proportionality

One of the most common misconceptions about neutrality is equating it with equal representation. The Neutral Point of View policy explicitly states that viewpoints must be represented "fairly and proportionately" , not necessarily to an equal degree. This paper proposes the concept of "representational proportionality" to formulate this principle precisely: the amount of space allocated to each viewpoint should be proportionate to that viewpoint's documented, epistemic standing within the body of reliable sources, rather than to the editor's desire for apparent balance. Assigning equal weight to a minority viewpoint and a consensus viewpoint is itself a form of bias, known in the literature as "false balance."

To clarify the difference between equal representation and representational proportionality, consider the following example:

Suppose that, among the reliable sources on a given scientific theory, ninety percent of researchers accept it and ten percent criticize it. Equal representation would mean allocating equal space to both viewpoints in the encyclopedic text; but such an approach, by creating the impression that the two viewpoints carry equal scientific standing, itself amounts to a form of bias. Representational proportionality, by contrast, requires that the majority viewpoint be reflected in greater detail, while the minority viewpoint is also acknowledged—but in smaller proportion.

Representational proportionality, therefore, separates neutrality from statistical neutrality: the goal is not to construct an artificial midpoint between viewpoints, but to honestly reflect their relative weight within the documented body of knowledge.

15. Neutrality Auditability

The complementary concept to representational proportionality is "neutrality auditability": the ability to trace an encyclopedic proposition backward, from the final text to the original source and to the editorial decisions made at each of the ten stages of the chain. A text may appear neutral on its surface, but if the path from source to proposition cannot be reconstructed, its claim to neutrality remains unverifiable. In this sense, auditability turns neutrality from a perceived quality into a demonstrable property: for every proposition, it must be possible to answer which source it was extracted from, whether it belongs to a viewpoint or a fact, whether the necessary attribution has been made, and whether anything has been added in the rewriting or framing beyond what the source states.

The importance of this concept is redoubled in the current period, in which content production assisted by large language models has been increasing. The English Wikipedia community has, since 2022, faced a growing challenge from content generated by language models—content that may lack neutrality, contain fabricated sources, or be laden with unwarranted generalizations ^[9]. Recent studies show that the Wikipedia community's efforts to establish comprehensive policies on AI-based content have repeatedly failed, ultimately resulting only in narrow, ad hoc rules, such as the speedy-deletion criterion for pages bearing obvious signs of machine generation ^[10]. This situation shows that, without a process-based framework for auditing, encyclopedic communities cannot systematically determine at which stage of the production chain a given text has deviated from the path of neutrality ^[11][12][13].

16. Applications of the Framework

As a conceptual framework, neutrality engineering has three main applications. First, it can provide a basis for developing empirical measures of neutrality; rather than assessing neutrality solely at the level of the wording of the final sentence, a separate measure can be defined for each of the ten stages of the chain, such as the proportion of propositions with explicit attribution, or the ratio of space allocated to a given viewpoint relative to its standing in the sources. Second, this framework provides a basis for comparative studies among collaborative encyclopedias or among different language editions of a single encyclopedia; one could examine at which stage of the chain—source selection, coverage, or framing—the cross-linguistic differences documented in studies such as Callahan and Herring ^[1] have the greatest effect.

Third, and perhaps most important under current conditions, this framework can serve as a basis for designing AI-based tools for auditing encyclopedic content. Rather than merely scanning the final text for the presence of biased vocabulary, such a tool could test each proposition against the ten stages of the chain: is the relevant source reliable and diverse? Has the proposition been correctly extracted? Has the viewpoint been correctly identified and attributed? Has an inference been made beyond what the source states? Such an approach, particularly in the face of the growing volume of content generated by language models, could complement human auditing mechanisms rather than replace them.

17. Conclusion

This entry has argued that neutrality in collaborative encyclopedias should not be treated merely as a stylistic rule or a normative policy, but should instead be regarded as the cumulative product of a ten-stage chain of editorial decisions: source selection, proposition extraction, viewpoint identification, attribution, rewriting, framing, control of evaluative language, control of inference, coverage and omission, and representational proportionality. The literature review showed that normative encyclopedic policies , computational bias-detection research ^[1][3][4], cross-cultural comparative studies ^[1], and research on the social dynamics of editing ^[5][6] each illuminate part of this chain, but have not, until now, been combined into a single, integrated model.

The concept of representational proportionality distinguished neutrality from equal representation and showed that genuine neutrality requires an honest reflection of the relative weight of viewpoints within the body of reliable sources, rather than the creation of an artificial balance. The concept of neutrality auditability, in turn, transformed neutrality from a perceived quality into a traceable and demonstrable property—one that is of growing importance, particularly in an era of expanding AI-generated content ^[9][10].

The neutrality-engineering framework is, ultimately, an invitation to redefine neutrality: not as a property sought at the end of the process, in the final sentence, but as a property that must be consciously designed and audited at every step of the encyclopedic knowledge-production process.