Claim LD04-SL-C18 (v1.0)
A two-year comparative ethnography of robotic and open surgery, with interviews at 13 US teaching hospitals, found that approved routes for learning failed in robotic surgery; only a minority of trainees came to competence, through tolerated but unapproved practices, and these practices led to hyperspecialisation and a shrinking supply of experts relative to demand.
Research opportunities this claim motivates
What this claim leaves open for research. Each is a hypothesis about a gap, not a finding.
- Do newcomers get less feedback and real work once AI coding tools arrive?In robotic surgery, approved learning routes failed when the technology removed trainees from hands-on work, and the supply of experts shrank: the mechanism this study would test for generative AI.
Provenance and status
- Claim type
- FACT + INTERPRETATION (as printed: “FACT + INTERPRETATION”)
- Publication status
- ELIGIBLE (STRONG_SCOPE) (as printed: “ELIGIBLE WITH STRONG SCOPE QUALIFIER”)
- Verification state
- CONTENT_VERIFIED_PRIMARY (ABSTRACT_ONLY) (as printed: “CONTENT VERIFIED ABSTRACT”; verified by: UNKNOWN)
- Does not license
- Any claim about generative AI; a measured effect size
- Source (field 6, as printed)
- Beane, “Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail,” Administrative Science Quarterly 64(1), 2019
- Source type (field 7)
- PEER_REVIEWED_JOURNAL_ARTICLE (as printed: “Peer-reviewed journal article”)
- Institutional position
- Independent academic
- Maturity (claim evidence / publication maturity)
- MATURE (as printed: “MATURE”)
- Recorded date
- 2026-10-14
- Currentness / correction
- This is the current version. No correction or concern relation is recorded on its Sources.
Version
Current version v1.0, resolved through the explicit current pointer CLAIM--LD04-SL-C18.
Pinned Sources (field 6, version-specific)
- “Beane, “Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail,” Administrative Science Quarterly 64(1), 2019” → SRC-SL-BEANE-ASQ-2019--v1.0 — Beane, “Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail,” Administrative Science Quarterly 64(1), 2019; class PEER_REVIEWED_JOURNAL_ARTICLE (explicit public provenance pin)
All 25 canonical Claim fields (raw values and normalized values)
| # | Canonical field | Canonical value (verbatim) | Normalized (Claim Value Vocabulary v1.0.2) |
|---|---|---|---|
| 1 | CLAIM ID | LD04-SL-C18 | — |
| 2 | PUBLIC-FACING CLAIM | A two-year comparative ethnography of robotic and open surgery, with interviews at 13 US teaching hospitals, found that approved routes for learning failed in robotic surgery; only a minority of trainees came to competence, through tolerated but unapproved practices, and these practices led to hyperspecialisation and a shrinking supply of experts relative to demand. | — |
| 3 | CLAIM TYPE | FACT + INTERPRETATION | base_types: FACT, INTERPRETATION |
| 4 | RESEARCH STRAND | D — Mechanisms and theory | — |
| 5 | MATURITY | MATURE | states: MATURE |
| 6 | SOURCE | Beane, “Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail,” Administrative Science Quarterly 64(1), 2019 | source_ids: SRC-SL-BEANE-ASQ-2019 |
| 7 | SOURCE TYPE | Peer-reviewed journal article | classes: PEER_REVIEWED_JOURNAL_ARTICLE |
| 8 | INSTITUTIONAL POSITION | Independent academic | — |
| 9 | SCOPE | Robotic surgery training at teaching hospitals; not generative AI | — |
| 10 | DESIGN | Two-year comparative ethnography at five sites plus blinded interviews at 13 top-tier US teaching hospitals | — |
| 11 | SAMPLE / DATASET | Surgical trainees and surgeons | — |
| 12 | MODEL / TOOL / VERSION | Surgical robots | — |
| 13 | COMPARATOR | Approved vs unapproved learning practices | — |
| 14 | MAIN RESULT | Approved learning routes worked in open surgery but failed in robotic surgery; only a minority of robotic trainees came to competence, through premature specialisation, abstract rehearsal and undersupervised struggle; these practices were neither punished nor forbidden and led to hyperspecialisation and a decreasing supply of experts relative to demand | — |
| 15 | INFERENTIAL TARGET | How a technology that removes novice participation changes the reproduction of expertise | — |
| 16 | DOES NOT LICENSE | Any claim about generative AI; a measured effect size | — |
| 17 | VERIFICATION STATE | CONTENT VERIFIED ABSTRACT | state: CONTENT_VERIFIED_PRIMARY · qualifiers: ABSTRACT_ONLY · verified_by: UNKNOWN |
| 18 | LIMITATIONS | Qualitative; surgical robotics only; checked against the official abstract only, read in the article's DOI record (Crossref) on 14 October 2026; the full text was not read | — |
| 19 | CONTRADICTORY / SUPPORTING SOURCES | The canonical mechanism behind the apprenticeship-gap position; not yet tested for generative AI | — |
| 20 | GEOGRAPHY / LANGUAGE | US / English | — |
| 21 | DISTRIBUTIONAL LEDGER | Only a minority of robotic trainees came to competence | — |
| 22 | R01 / FIELD-MAP LINKS | LD-04, D2, D3 | — |
| 23 | WHAT WOULD CHANGE THIS CLAIM? | Not applicable as a study fact; its relevance to generative AI would change with direct evidence on how AI changes junior participation | — |
| 24 | PUBLICATION STATUS | ELIGIBLE WITH STRONG SCOPE QUALIFIER | state: ELIGIBLE · qualifiers: STRONG_SCOPE |
| 25 | RECORDED DATE | 2026-10-14 | — |
Field 22 references resolved against Field Map v0.2: LD-04 (LIVING_DEBATE); D2 (DOMAIN); D3 (DOMAIN)
Cited by Living Debate versions
Research-of-record provenance
Materialized by sociolens-materializer 0.6.0 at 2026-09-28T20:00:00.000Z; normalization contract SocioLens — Claim Value Vocabulary v1.0.2 — Pre-Spike Materialization Contract (v1.0.2). Content seal SHA-256 9c9f6650e3b913633d32476d6ad39c9083836d84b275a3a404cb0c4a3bdc96ba.
Repository-authored inputs (not Drive exports)
- SocioLens claim — LD04-SL-C18 v1.0 (v1.0) — written in the SocioLens repository by Project Lead (Claude, AI) on 2026-10-14; no Drive identity exists. Authority: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 14 October 2026 to build the LD-04 debate; a new Claim, first version, written from the project's LD-04 source notes, checked against full texts or official abstracts. Block “ENTRY 18 — LD04-SL-C18” SHA-256
622dbe46c09c0b68027f01d8e5cd54da49e7cbafd8711e4fc4b8b9e4e937bd47; fixturefixtures/authored/claims/LD04-SL-C18--v1.0.
Builder decisions (not Drive data)
- VERSION = v1.0 — basis: Claim version taken from the repository-authored claim fixture version (SocioLens claim — LD04-SL-C18 v1.0); the first version of a new Claim of the LD-04 debate
- CURRENT_POINTER = true — basis: repository-authored claim "SocioLens claim — LD04-SL-C18 v1.0" (Project Lead (Claude, AI), 2026-10-14); basis: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 14 October 2026 to build the LD-04 debate; a new Claim, first version, written from the project's LD-04 source notes, checked against full texts or official abstracts
- FIELD_6_SOURCE_REFERENCE = SRC-SL-BEANE-ASQ-2019--v1.0 — basis: field 6 of ENTRY 18 — LD04-SL-C18 cites "Beane, “Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail,” Administrative Science Quarterly 64(1), 2019"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
- SOURCE_PROVENANCE_PIN = SRC-SL-BEANE-ASQ-2019--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Beane, “Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail,” Administrative Science Quarterly 64(1), 2019".