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This library entry is part of The Extended Frontier thesis. Entries are curated with AI assistance and human review; most initial entries were prepared with Claude (Anthropic), while individual entries may note other assisting systems. Metadata and annotations are editorial, not peer-reviewed. Entries flagged as unverified may contain placeholder dates, authors, or classifications.

Building an AI-ready public workforce

OECD··doc·source
Metadata unverified. Publisher and URL verified. Date is a best-estimate; confirm the publication date from the OECD page before citing.

OECD full report on how public-sector workforces are (and are not) prepared to deploy AI. Brought into the library as a governance-piece anchor: the argument is that whether an AI system is capable *in practice* depends on the institutional scaffolding around its use, not only on the model or the harness.

Classification

Role
governance-piece
Domain
operations
Source type
doc
Harness types
ratification-harnesssocial-harnessmonitoring-harness
Validation position
before-actionpost-deploymentcontinuous
Validation mode
institutionalsocial
Prescription stance
strongly-procedural
Relation to argument
institutions-shape-capabilitydiffusion-adoption-bottleneckbreakdown-when-harness-absent
Tags
workforcepublic-sectoroecdinstitutional-scaffoldinggovernance

Extended capability commentary

Input legibility
Task structure
Reward richness
Public-sector outcomes rarely collapse to a cardinal reward.
Feedback latency
Policy-level feedback is slow. Years, not cycles.
Repairability
Observability
Institutional ratification
The report is itself a ratification instrument.

Why it matters

Counterweight to the software-centric pole of the library. A large portion of real AI deployment lives inside institutions whose capability depends on workforce preparation, training, accountability, and procurement — none of which is captured by 'harness' in the coding-agent sense.

Annotation

A governance entry. Places the question of AI capability-in-practice inside the frame of public administration: whether AI makes a public-sector system more capable depends on training, data integration, procurement norms, and public-private partnership structures, not only on the model or its harness.

The OECD framing forces the library to reckon with a kind of scaffolding that coding-agent practitioners rarely name:

  • Workforce training as a first-mile input-formation mechanism.
  • Accountability procedures as a ratification harness with legal and political standing.
  • Cross-agency data integration as a grounding-and-context-loading substrate.

Why it pairs with the software entries

  • Tan, thin harness / fat skills — highlights the domain mismatch: thin-harness prescriptions assume a software-practitioner user. Here the "user" is a multi-layered public institution.
  • HumanLayer, "Skill Issue" — both pieces agree that the harness matters; they disagree about which harness.

Related entries

Overlap is computed on tags, relation-to-argument, and harness types — not on role or domain, because contrasts are often the most useful neighbours.