Tony and Jenn are especially effective as a team because they cover both the technology and operational sides of scholarly publishing. Tony brings systems and product thinking across the full publishing stack—interoperability, platform strategy, practical AI implementation, and research-integrity tooling ecosystems—while Jenn brings deep editorial operations leadership, scalable service delivery, and the execution discipline that comes from growing a small operation into a mature organization. Together, they can lead end-to-end engagements that diagnose what’s not working, design a pragmatic solution, and implement it in a way that balances editorial reality with platform and automation leverage.
clarify mission, prioritize initiatives, define success metrics, build a 12–24 month execution roadmap
org design, role clarity, governance, decision cadence, and scalable processes for editorial/production teams
requirements definition, RFP/RFI support, scoring frameworks, demos, reference checks, and contract/SLAs guidance
submission → peer review → decisioning → production → publication, with bottleneck analysis and measurable improvement plans
author/editor guidance, reviewer policies, decision consistency, escalation paths, and audit-ready documentation
training programs for editors and staff, change-management planning, and playbooks that stick
single/double-blind, open review, portable review, preprint-first workflows, cascading, and reviewer recognition strategies
recruitment tactics, incentives, onboarding, and quality signals; scaling reviewer operations for growth
rubric design, editorial calibration, and QA mechanisms that reduce rework and complaints
policy + process for plagiarism, image manipulation, paper mills, authorship concerns, data availability, ethics, and COI
how to combine third-party integrity tools and internal checks into a coherent “single pane of glass” workflow
triage, escalation, documentation, and communications templates aligned with COPE-style best practices
permissible use, disclosure language, human oversight, risk levels, and operational controls
where AI actually saves time (triage, reviewer matching, QA checks, summarization, taxonomy tagging) and how to implement safely
guidance on controlled AI access to licensed content, APIs, and agent-mediated workflows
expectation setting, QBR narratives, roadmap communication, and escalations
crisp strategy decks, business cases, and decision memos
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