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)

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.
What work do junior staff in Greek firms still learn by doing once AI arrives?When surgical robots took trainees out of hands-on work, approved routes for learning failed and only a minority of trainees reached competence, through tolerated but unapproved practice.

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)
Read the original

Version

This is the current version, v1.0. Earlier versions stay available, and their records never change.

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".