Qualitative analysis of CRT in librarianship

Problem: Scholars and library teams want to move beyond DEI optics to measurable changes. In a detailed August 20, 2025 essay, Maria Mejia and Anastasia Chiu apply critical race theory (CRT) frameworks (interest convergence, intersectionality, and counter-storytelling) to scholarly communications at a predominantly white institution (www.inthelibrarywiththeleadpipe.org/2025/interest-convergence/). Payoff: this post shows how to run a rigorous qualitative analysis of CRT in librarianship to surface who benefits, where labor is hidden, and which policy levers matter, and how to operationalize that analysis in Evidano (www.evidano.com) for secure, explainable results.

Fast take + source

What happened: On 20 August 2025 two scholarly communications practitioners of color published a practice-focused CRT essay that combines lived workplace examples with prescriptive practices for departments and instruction.

Source: Mejia & Chiu, In the Library with the Lead Pipe, www.inthelibrarywiththeleadpipe.org/2025/interest-convergence/
Primary frameworks: interest convergence, intersectionality, counter-storytelling
Immediate use case: analyze staff narratives, instruction materials, and policy documents to find mismatches between DEI rhetoric and practice

Findings snapshot

Date	Authors	Corpus	Key frameworks	Notable facts	Source
20 Aug 2025	Maria Mejia & Anastasia Chiu	Scholarly essay + departmental examples	Interest convergence; Intersectionality; Counter-storytelling	Dept comprised entirely of BIPOC; institution PWI; public DEI statement removed in 2025	www.inthelibrarywiththeleadpipe.org/2025/interest-convergence/

What happened (plain English)

Mejia and Chiu frame everyday library work (teaching copyright, citation, and open access) as racialized practices. They argue that institutions often adopt DEI language only when it aligns with institutional interests (interest convergence), and that BIPOC staff absorb disproportionate, often invisible emotional and service labor.

They use intersectionality to show how overlapping identities (race, gender, class) compound labor and risk.
Counter-storytelling is proposed as both pedagogy (e.g., citation politics) and workplace strategy (documenting invisible labor for promotion cases).
They recommend tactical boundary-setting, documenting emotional labor, and using interest convergence selectively to advance antiracist aims.

So what for researchers, UX teams, and policy analysts: qualitative analysis of CRT in librarianship

For researchers & evaluators

Use CRT frameworks as coding lenses to expose who benefits from initiatives. Tag excerpts that show: promises vs. implemented changes; which groups perform DEI labor; and where tokenization occurs.

Look for patterns in promotion dossiers, committee invites, and service loads to quantify invisible labor.

For UX and instruction teams

Audit workshops and teaching materials for citational bias and gatekeeping. Code examples of who is cited and who is omitted; surface alternatives and counter-stories to reframe curricula.

Segment feedback by identity and role to spot divergent experiences (students, adjuncts, tenure-track).

For policy and HR

Map interest-convergence signals: where did leadership endorse a change and what institutional benefit did it promise? Use that mapping to design policies that lock in material gains for BIPOC staff.

Use documented counter-stories as qualitative evidence in tenure/promotion and budgeting cases.

Do more, faster with Evidano

Ingest & prepare

Import essays, meeting transcripts, email collections, and promotion dossiers into Evidano. Use transcription with custom dictionaries and PII redaction when you convert audio or video to text.

Translate multilingual inputs with custom dictionaries to preserve key terms and names.

Code with CRT lenses

Upload a CRT codebook (interest convergence, intersectionality, counter-storytelling) and run AI-assisted coding across your corpus. Evidano produces hierarchical codes → subcodes so you can trace themes to quotes.

Export coded excerpts for counter-story compilations or promotion evidence.

Compare segments & quantify

Run cross-segment analyses (by role, hire type, race/gender where ethically permitted) to measure who carries extra service load or emotional labor.

Use frequency, co-occurrence networks, and word clouds to validate narratives and present digestible evidence to stakeholders.

Secure, explainable, repeatable

All data is encrypted and never used to train third-party models. Generate reproducible reports and clickable quotes for decision memos and faculty dossiers.

If you need follow-up data, deploy AI avatar interviews to collect targeted qualitative responses at scale while preserving consent flows.

Checklist: 7-step workflow to reproduce Mejia & Chiu’s analysis

Run this in a 2–3 week pilot to surface gaps between DEI rhetoric and lived practice.

1) Gather corpus: policy docs, DEI statements, meeting notes, promotion files, training materials, and relevant publications (start with Mejia & Chiu, 20 Aug 2025).
2) Transcribe/translate audio with custom dictionary and PII redaction.
3) Import or build a CRT codebook (interest convergence; intersectionality; counter-storytelling).
4) Run AI-assisted coding and review high-confidence excerpts; adjust code labels.
5) Run cross-segment analysis (role, appointment type, year hired) to quantify service loads and citation patterns.
6) Visualize co-occurrence networks and export counter-story collections for stakeholders.
7) Draft actionable recommendations: boundary policies, promotion evidence templates, and curricular changes.

FAQ: common questions about qualitative analysis of CRT in librarianship

How do I compare segments without violating privacy?

Aggregate where possible; redact identifiers; use statistical thresholds (e.g., report only aggregates with n≥5) and secure access controls.

Can AI detect 'interest convergence' language?

Yes, with a tailored codebook, AI can flag framing that ties equity actions to institutional benefits. Human review is required to interpret intent.

How do I use counter-stories in promotion materials ethically?

Obtain consent, anonymize when requested, and include contextual metadata (date, committee, role) so reviewers can assess scope and impact.

Wrapping up: next moves

If your team needs to surface the mismatch between DEI language and lived workplace experience, start by building a lightweight corpus and running the 7-step workflow above in Evidano.

Audit one policy or one course module in two weeks to produce a stakeholder-ready brief.
Use www.evidano.com to pilot secure, explainable thematic and cross-segment analyses; export visuals and quote collections for decision-makers.

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