Collaborative Autoethnography

• Collaborative autoethnography is a qualitative method where teams systematically document and analyze lived experiences to interrogate social, technological, or policy issues.
• It employs deliberate heterogeneity and peer cross-coding to enhance analytical triangulation and mitigate individual bias in narrative synthesis.
• The approach is pivotal in fields like HCI and accessibility studies, offering a framework for ethical engagement and epistemic justice.

Collaborative autoethnography is a qualitative research methodology in which a team of co-researchers systematically generate, share, and jointly analyze personal narratives to interrogate questions of social, technological, or policy relevance. Unlike traditional autoethnography, which centers on the lived experience and reflexivity of a single researcher, collaborative variants deliberately recruit heterogeneous teams to cross-check interpretations, surface divergent perspectives, and produce richer thematic syntheses that span multiple vantage points. The method now plays a foundational role across HCI, accessibility studies, and science-practice integration, especially where issues of epistemic injustice, marginalization, or practitioner-researcher engagement are central (Glazko et al., 2023, Ajmani et al., 24 Jan 2025, Russo et al., 2024).

1. Theoretical Foundations

Collaborative autoethnography is grounded in the intersection of ethnography (the immersive study of social phenomena), autoethnography (systematic self-analysis and narrative inscription), and collective reflexivity. Its most immediate theoretical lineage traces to Chang, Ngunjiri, and Hernandez (2013) and is further developed within the qualitative social sciences and HCI, especially through the lens of reflexive thematic analysis (Braun & Clarke 2006, 2022) (Russo et al., 2024).

Key epistemological frameworks for collaborative autoethnography include:

• Dialogical autonomy (feminist ethics): Autonomy is understood as emergent within accountable relations, not as atomistic self-rule (Ajmani et al., 24 Jan 2025).
• Virtue-epistemic accounts: These interrogate how knowledge production and injustice are linked through testimonial and hermeneutical harms.
• Participatory and action research: Methodological traditions emphasizing co-creation, dynamic consent, and the redistribution of analytic authority toward community members (Russo et al., 2024).

The methodology is particularly salient in research contexts where marginalized or practitioner communities have historically been excluded, pathologized, or misrepresented.

2. Distinguishing Methodological Features

Collaborative autoethnography departs from the “lone-researcher” model through several core features (Glazko et al., 2023):

• Multiplex Subjectivity: Each team member functions simultaneously as subject (contributor of lived experience) and analyst (interpreter/coder of others’ diaries).
• Deliberate Heterogeneity: Teams are recruited along lines of ability, seniority, social location (e.g., disability, gender, immigrant status), enhancing analytical triangulation and minimizing mono-perspectival blind spots.
• Cross-Checking and Consensus: Reflexive coding is performed by peers, with codes and emergent themes compared, reconciled, and validated via plenary sessions.
• Anonymized Amalgam Vignettes: To maintain confidentiality and elevate the analytic over the anecdotal, narrative examples are constructed by amalgamating multiple journals rather than attributing to individuals.
• Iterative Group Reflection: Recurring meetings ensure ongoing ethical scrutiny, accountability, and adaptation to evolving group dynamics.

A plausible implication is that the collaborative model privileges processual rigor and epistemic humility over traditional forms of “objectivity,” relying instead on negotiated consensus and transparent reporting of failures as well as successes.

3. Canonical Workflows and Formal Models

Procedural instantiations of collaborative autoethnography exhibit substantial uniformity across leading studies (Glazko et al., 2023, Ajmani et al., 24 Jan 2025, Russo et al., 2024):

Step	Activity/Operation	Example Reference
1	Team formation with diverse researcher-participants	(Glazko et al., 2023)
2	Structured journaling and data collection	(Glazko et al., 2023, Ajmani et al., 24 Jan 2025)
3	Regular collective reflection meetings	(Glazko et al., 2023, Russo et al., 2024)
4	Cross-coding and collaborative thematic analysis	(Glazko et al., 2023, Ajmani et al., 24 Jan 2025)
5	Vignette/amalgam construction & consensus review	(Glazko et al., 2023, Ajmani et al., 24 Jan 2025)

A widely adopted workflow is:

1. Recruit a deliberately heterogeneous team of subject-experts.
2. Solicit independent autoethnographic journals, ensuring comparability via shared cloud-based templates.
3. Conduct regular debriefs focused on ethical questions, emergent patterns, and the surfacing of power dynamics.
4. Engage in peer coding: each narrative is analyzed by at least one other team member, with open codes for key dimensions (e.g., bias, verification difficulty, epistemic autonomy).
5. Synthesize findings into anonymized vignettes for public or intra-community dissemination, with iterative member checking.

Formalization is possible: for epistemic autonomy-centered designs, protocols may be indexed by a weighted sum of co-authorship ($C_{\text{coauth}}$), iterative member checking ($C_{\text{memberCheck}}$), participant governance over data ($C_{\text{dataGovern}}$), and depth of reflexivity ($C_{\text{reflexivity}}$), as follows (Ajmani et al., 24 Jan 2025): $$AI = w_1\,C_{\text{coauth}} + w_2\,C_{\text{memberCheck}} + w_3\,C_{\text{dataGovern}} + w_4\,C_{\text{reflexivity}}$$ where $w_i$ are weights reflecting methodological priorities.

4. Key Applications and Case Studies

Accessibility and Technological Mediation

A consequential deployment appears in the assessment of generative AI’s accessibility impact for disabled and non-disabled users (Glazko et al., 2023). Over three months, a team spanning multiple impairments and identities recorded daily GAI tool use, which was then cross-coded and thematically synthesized. This surfaced distinctive findings:

• Consensus-building on verifiability: Ease of verifying low-stakes outputs contrasted with difficulties in validating nuanced accessibility tasks.
• Detecting subtle ableism: Collaborative review exposed ableist outcomes invisible to any one individual’s account.
• Tool for advancing “epistemic autonomy”: By involving subjects as co-analysts, collaborative autoethnography foregrounded user agency against ableist design defaults.

Epistemic Autonomy and Marginalization

The methodology has been expanded to operationalize “epistemic autonomy” for marginalized communities. Here, co-researchers (e.g., trans women in HCI) co-design, co-analyze, and co-author all stages, ensuring narrative control and dynamic, ongoing consent (Ajmani et al., 24 Jan 2025). This model aims to redress testimonial and hermeneutical injustices produced by external epistemic authorities, establishing a continuum for measuring autonomy in research design.

Bridging Research and Practice

Collaborative autoethnography structures the reflexive study of researcher-practitioner partnerships, as demonstrated by Russo et al. (2024) (Russo et al., 2024). Here, quantitative researchers used the approach to dissect their own conversational engagement with domain experts across fields. Analysis clarified key vectors of effective bridging: valuing non-academic expertise, navigating diverging objectives and timelines, avoiding data extractivism, and recognizing the limitations of quantification.

5. Ethical, Epistemic, and Practical Challenges

Implementation of collaborative autoethnography is characterized by distinctive ethical and operational complexities:

• Actor-observer dual roles: Each contributor must balance subjective narrative disclosure with analytic detachment.
• Power, confidentiality, and “safe space” protocols: Regular check-ins, rotation of analytic responsibility, and anonymization strategies are routine (Glazko et al., 2023).
• Dynamic consent: Consent is iteratively renegotiated at each analysis and dissemination milestone; withdrawal and redaction rights are explicit (Ajmani et al., 24 Jan 2025).
• Labor and emotional burden: Collaborative analysis requires significant time and emotional investment, raising scope and sustainability considerations.
• Potential for tokenism: Without vigilant co-authorship and member-checking, marginalized voices may be improperly framed or subsumed (Russo et al., 2024).

Ethical best practices highlighted in empirical studies include: budgeting for participant compensation and care, archiving analytic decisions, and embedding rigorous debriefing and bias-auditing throughout (Ajmani et al., 24 Jan 2025, Glazko et al., 2023).

6. Practical Recommendations and Extensions

Derived from lived experiments in accessibility and HCI, key recommendations are widely transferable (Glazko et al., 2023, Ajmani et al., 24 Jan 2025, Russo et al., 2024):

• Begin with a deliberately heterogeneous team and avoid analytic dominance of any sub-group.
• Standardize data collection via templates to ensure journal comparability.
• Schedule regular, brief meetings to surface emergent ethical or methodological tensions.
• Institutionalize co-coding and reciprocal analysis for early bias detection.
• Construct narrative vignettes that amalgamate experiences, protecting identities while retaining narrative power.
• Develop verification routines along a “stakes” continuum and design accessible validation mechanisms accordingly.
• Report both methodological and substantive failures with equal granularity and candor.

Extensions include integration with participatory action research and asynchronous remote communities, the construction of formal autonomy indexes for protocol evaluation, and deployment as a curricular scaffold for research-practice engagement in graduate education (Ajmani et al., 24 Jan 2025, Russo et al., 2024).

7. Significance and Future Trajectories

Collaborative autoethnography has catalyzed a step change in research cultures that demand negotiated, pluralist approaches to sensemaking, especially where histories of epistemic injustice or extractivism have excluded participant communities. The methodology’s analytic signature lies in its fusion of multiplex subjectivity, procedural co-governance, and structured reflexivity, all operationalized through robust group processes, cross-coding, and consensus review.

Emergent trends include the formal quantification of “epistemic autonomy” via analytic indices, integration with asynchronous and remote participation infrastructures, and the hybridization with participatory method clusters to institutionalize ethical knowledge co-production. These developments underscore collaborative autoethnography’s expanding role in rigorous, justice-oriented social and technical inquiry (Glazko et al., 2023, Ajmani et al., 24 Jan 2025, Russo et al., 2024).