Inside the APC Trap: Qualitative analysis of APCs

Evidano is an AI-powered qualitative data analysis platform that helps teams reproduce this PLOS ONE study’s pipeline and analyze open-ended responses securely. The PLOS ONE study identifies an "APC Trap", a cognitive bind where researchers feel obligated to pay article processing charges (APCs) despite recognizing inequities and unsustainability, based on 154 substantive open responses coded into eight themes. The qualitative analysis shows 90% of substantive responses contained at least one APC Trap code and reveals layered, co-occurring concerns across disciplines and publishing types.

• Published: July 1, 2026, DOI: 10.1371/journal.pone.0351430, source: PLOS ONE.
• Dataset: survey collected Oct 31–Nov 21, 2023; 322 completed surveys and 154 substantive open-ended responses were coded inductively.
• Eight APC Trap codes were identified and applied at the respondent level; 90% of substantive responses (n=139) contained ≥1 APC Trap code, 62% had 1–2 codes, and 28% had ≥3 codes.

The authors used purposive sampling across four U.S. public R1 universities to collect tailored survey responses and combine open-ended prompts into analyzable qualitative data. The authors ran six tailored Qualtrics surveys (by discipline and OA type), aggregated two open-ended prompts per respondent into a single qualitative response (n=154), and applied inductive thematic coding at the respondent level. Inductive thematic coding produced eight APC Trap codes that coders applied yes/no per respondent using consensus (at least four coders) with iterative Code Book refinement, and analyses included code frequencies, co-occurrence matrices, and disciplinary/institutional cross-tabs; automated sentiment tools were tested but found unreliable for these nuanced responses.

Follow these steps to replicate the study’s core outputs in Evidano.

• 1) Upload Qualtrics CSVs and map respondent IDs plus institution and discipline tags.
• 2) Normalize and merge the two open-response fields into a single text field per respondent.
• 3) Run an inductive theme extraction to surface candidate codes and export suggestions.
• 4) Import or edit a Code Book then run batch coding and resolve disagreements with Evidano’s consensus reviewer tool.
• 5) Generate frequency tables and cross-segment comparisons (Gold vs Hybrid, HSS/HM/NSE).
• 6) Produce a co-occurrence network and export representative quotes per code for transparency.
• 7) Create stakeholder-ready visuals and an executive brief; iterate with AI chat over your documents to draft policy recommendations.

The PLOS ONE paper (published July 1, 2026) offers a replicable qualitative frame, the APC Trap, useful for anyone measuring the lived effects of publishing costs. If your team analyzes open comments, policy feedback, or interview transcripts, apply respondent-level multi-label coding, co-occurrence analysis, and segment comparisons to reveal the layered bind the authors describe.

• Ready to pilot? Reproduce the study’s pipeline end-to-end in Evidano and produce shareable visuals and evidence-backed recommendations quickly: Try Evidano for free.
• Ethics note: this post summarizes research findings for methodological and operational use; follow IRB and consent guidance when reusing participant data.