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.

SocioLens project,

Claim LD04-SL-C18 (v1.0)

Provenance and status

Claim type: Fact and Interpretation
Publication status: Eligible for publication, with qualifiers: strong scope
Verification state: Checked against the abstract of the primary source only. Who checked this claim is not recorded for it; claim checks so far were AI-led, and no person has checked claims against their sources.
Does not license: Any claim about generative AI; a measured effect size
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
Source (as cited): Beane, “Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail,” Administrative Science Quarterly 64(1), 2019
Source type: Peer-reviewed journal article (recorded as “Peer-reviewed journal article”)
Institutional position: Independent academic
Maturity of the evidence: Mature evidence
Recorded date: 14 October 2026
Currentness / correction: This is the current version. No correction or concern is recorded on its sources.

Sources this version cites

Beane, “Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail,” Administrative Science Quarterly 64(1), 2019 (Peer-reviewed journal article)
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Version

This page shows version v1.0. Its record never changes, even when newer evidence arrives; the site may add a note to the page or withhold a figure not cleared for public use.

This pinned version is also the current version.

Versions: v1.0 · current

Raw record: every recorded field, with its coded values

All 25 canonical Claim fields, verbatim, with the coded values derived from them. The plain-language labels above are read from these.

Claim type (coded): FACT + INTERPRETATION (as printed: “FACT + INTERPRETATION”)
Publication status (coded): ELIGIBLE (STRONG_SCOPE) (as printed: “ELIGIBLE WITH STRONG SCOPE QUALIFIER”)
Verification state (coded): CONTENT_VERIFIED_PRIMARY (ABSTRACT_ONLY)
Source type (coded): PEER_REVIEWED_JOURNAL_ARTICLE
Maturity (coded): MATURE

#	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)

The sources as recorded, with their record IDs and coded source types:

Pinned Sources (field 6, version-specific)

“Beane, “Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail,” Administrative Science Quarterly 64(1), 2019” → Beane (2019), “Shadow Learning: Building Robotic Surgical…” (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)

Cited by Living Debate versions

Cite this page

SocioLens project. (2026, October 14). 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 [Claim LD04-SL-C18, version v1.0]. SocioLens. https://sociolens-web.pages.dev/claims/ld04-sl-c18/v1.0/

@misc{sociolens_ld04_sl_c18_v1_0,
  author = {{SocioLens project}},
  title = {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.},
  year = {2026},
  date = {2026-10-14},
  howpublished = {\url{https://sociolens-web.pages.dev/claims/ld04-sl-c18/v1.0/}},
  note = {Claim LD04-SL-C18, version v1.0}
}

The link points to this version, whose record never changes. For a finding, cite the original study as well: SocioLens summarizes research and does not replace it.

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; fixture fixtures/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".