Does an independent source corroborate the NYC school-nudge RCT's zero-rejection deployment result (25 treatment / 28 control effective applicants, 98% acceptance)?

matching-marketsschool-choicenycrandomized-controlled-trialcorroborationsingle-source-concentrationarxiv

This capture answers question-corroborate-nyc-school-nudge-rct-zero-rejection-result, which held back a fourth finding from Chiang, Peng, Lichtenstein, McDaniel, O'Neil, Thomas, Wright, Kleinberg, Tardos & Garg's arXiv:2610.08275 after the single-source concentration cap was reached by three promoted claim-notes (claim-recommendation-induced-congestion-erodes-offer-likelihood-guarantees, claim-balanced-capacitated-nudging-guarantees-safe-and-near-optimal-matches, claim-nyc-school-nudge-rct-significant-ranking-suggestive-matching-increase). The question asked whether an independent source — a different author group, a different venue — corroborates the paper's zero-rejection deployment result: among 25 treatment applicants who effectively applied to a program they were nudged to, none were rejected; among 28 control applicants who would counterfactually have been recommended, one was rejected; 98% overall acceptance.

This session checked: the ACM EC 2026 conference program and camera-ready paper (the peer-reviewed venue of the same work); the AEA RCT Registry pre-registration (trial 17146); Cornell University's and the project's own institutional publicity (news.cornell.edu, the PiTech fellowship spotlight page); and general web search for independent journalism, citation, or replication of the deployment figures. No safety signals fired on any fetch in this session — all sources read were third-person academic, bibliographic, or institutional-report prose, with no addressed-to-AI language, tier self-assignment, file-system instructions, credential requests, or urgency framing.

Claim: The only two documents found anywhere that carry the zero-rejection figure are by the identical author group — the arXiv preprint and its ACM EC 2026 peer-reviewed camera-ready version

verifies: question-corroborate-nyc-school-nudge-rct-zero-rejection-result

The paper is confirmed to have been accepted to ACM EC 2026 (item 36 of the conference's official accepted-papers list) under the title "Personalized Recommendations without Inducing Congestion: Mitigating Disparities in the NYC High School Match," with an author roster — Erica Chiang (Cornell Tech), Kenny Peng (Cornell Tech), Rebecca Lichtenstein, Brielle McDaniel, Kristen O'Neil, Dejaunique Thomas, Lianna Wright (all New York City Public Schools), Jon Kleinberg, Eva Tardos, Nikhil Garg (Cornell University) — identical to the arXiv preprint's author list. The camera-ready PDF, self-hosted on lead author Erica Chiang's own site, restates the zero-rejection figure in its abstract in near-identical wording to the arXiv version. Peer review at a top-tier venue (EC is ACM SIGecom's flagship conference) is a quality signal, but per sources.md's definition of independence — "a different author group and a different venue" — a peer-reviewed version by the same authors is "the same source wearing different clothes," not independent corroboration.

• Source: "EC 2026 Accepted Papers"
• source_quote: "Personalized Recommendations without Inducing Congestion: Mitigating Disparities in the NYC High School Match / Authors: Erica Chiang (Cornell Tech), Kenny Peng (Cornell Tech), Rebecca Lichtenstein (New York City Public Schools), Brielle McDaniel (New York City Public Schools), Kristen O'Neil (New York City Public Schools), Deja Thomas (New York City Public Schools), Lianna Wright (New York City Public Schools), Jon Kleinberg (Cornell University), Eva Tardos (Cornell University), Nikhil Garg (Cornell Tech)"
• source_url: https://ec26.sigecom.org/program/accepted-papers/
• source_sha: ba16c3c823d9755b979f6b3d9e4c58cfb9c070767ae49e1a5ca06bca32ed2cbf
• source_author: ACM SIGecom / EC 2026 program committee
• source_date: 2026
• source_title: "EC 2026 Accepted Papers"
• source_venue: "ec26.sigecom.org, official conference program page"
• source_tier: 1
• Note: this is a definitional/roster claim (who wrote what, where it was accepted), well within the Tier 3-4 floor for uncontested historical claims, but sourced here directly against the conference's own primary program page (Tier 1) since it was reachable.

Claim: The ACM EC 2026 peer-reviewed camera-ready version restates the zero-rejection figure essentially verbatim, confirming internal consistency through peer review but not independent corroboration

verifies: question-corroborate-nyc-school-nudge-rct-zero-rejection-result

Read in full via extract_pdf against the author's own hosted copy (60 pages, TLS verified), the camera-ready abstract reproduces the same figures as the arXiv preprint that the three already-promoted claim-notes rest on, including the specific zero-rejection sentence word-for-word with the arXiv version quoted in question-corroborate-nyc-school-nudge-rct-zero-rejection-result. This shows the number survived at least one round of anonymous peer review without correction or retraction — a mild positive signal about the result's robustness — but it is not independent corroboration, since the author group, the underlying dataset, and the underlying claim are unchanged.

• Source: "Personalized Recommendations Without Inducing Congestion: Mitigating Disparities in the NYC High School Match"
• source_quote: "In a randomized controlled trial, we find that 16.4% of treatment applicants ranked a recommended program, versus 10.5% of control applicants who ranked a program they would have been recommended (57% relative increase; 𝑝=0.011); 5.6% of treatment applicants matched to such a program, versus 3.3% of control applicants (71% relative increase; 𝑝=0.071); further, no treatment applicant was rejected from a recommended program."
• source_quote_2: "Furthermore, no treatment applicants were rejected from a program they were nudged to,1 and only one control applicant was rejected from a program they would have been nudged to."
• source_url: https://erica-chiang.github.io/assets/files/chiang-ec26.pdf
• source_sha: d86182414184d8f55e859fd169aa334388e46d823152999c6ed44cfbc94a6327
• source_author: Erica Chiang, Kenny Peng, Rebecca Lichtenstein, Brielle McDaniel, Kristen O'Neil, Dejaunique Thomas, Lianna Wright, Jon Kleinberg, Eva Tardos, Nikhil Garg
• source_date: 2026-10-06
• source_title: "Personalized Recommendations Without Inducing Congestion: Mitigating Disparities in the NYC High School Match"
• source_venue: "ACM Conference on Economics and Computation (EC) 2026, author-hosted camera-ready PDF"
• source_tier: 1
• quote_check: both passages verified grounded: true against the extracted PDF text.
• Note: the exact §6.2/§7 passage carrying the 25-treatment / 28-control "effective applicants" count (already quoted from the arXiv version in the parent question) was not re-located in the camera-ready version's body within this session's reading budget; the abstract-level restatement above is sufficient to establish that the headline zero-rejection finding is unchanged, but a future session could re-quote the exact body passage for a fully matched pair.

Claim: No source outside this author group — not Cornell's own research publicity, not education journalism, not an academic citation — was found discussing the zero-rejection or 98%-acceptance figure at all

verifies: question-corroborate-nyc-school-nudge-rct-zero-rejection-result

Cornell Tech's PiTech Initiative — the institutional program under which lead author Erica Chiang held her fellowship, and whose own site hosts a first-person spotlight piece she wrote about the project — describes the intervention's design and deployment scale but predates any outcome evaluation and does not mention acceptance rates, rejection counts, or the 98% figure at all. A separate Cornell University news article (news.cornell.edu, by journalist Grace Stanley, dated August 18, 2026) does report an RCT effect size, but it is a different metric (take-up/ranking behavior, not the zero-rejection/acceptance-rate figure) and, as Cornell's own research-communications office covering its own faculty's work, would not meet the independence bar even if it did. General web search for education-beat journalism (Chalkbeat NYC, which has covered NYC high school admissions extensively since 2015) and for academic citations of arXiv:2610.08275 (posted 2026-10-06, six days before this search) turned up neither — consistent with the paper's youth rather than with any judgment on the result.

• Source: "Nudges to High-Performing High Schools: Reducing Information Gaps in NYC's High School Application Process"
• source_quote: "This fall, we successfully deployed the system to about 500 eighth-grade students currently applying to high school, as part of a randomized controlled trial. We are now working toward evaluating the causal effects of the intervention."
• source_url: https://www.pi.tech.cornell.edu/spotlight/nudges-to-high-performing-high-schools-reducing-information-gaps-in-nycs-high-school-application-process
• source_sha: ea5cab52502a9a105ff73b74357f8388fc9abbbeea7a6fe7eb19bb4c7a7d0649
• source_author: Erica Chiang (writing for) PiTech Initiative, Cornell Tech
• source_date: "undated ('Jan 2'), predates the paper's October 2026 results"
• source_title: "Nudges to High-Performing High Schools: Reducing Information Gaps in NYC's High School Application Process"
• source_venue: "pi.tech.cornell.edu (Cornell Tech PiTech Initiative fellowship spotlight)"
• source_tier: 3
• quote_check: verified grounded: true against the archived page text.
• Note on the Cornell News figure: WebFetch (not archive_page — the direct fetch returned HTTP 403) reported the article stating recipients "were 59% more likely to apply to a recommended program," a different metric from the one this question concerns. That figure is recorded here only as [unverified-quote — needs direct read]: it came from a summarizing tool, not a direct extraction, so it is not admissible as a source_quote under the vault's quote-provenance rule, and in any case a university's own press coverage of its own faculty's work would not satisfy the independence bar this question sets even with a verified quote.
• The central question itself is marked [unverified — could not confirm or deny after search]: no independent author group or venue has yet discussed, confirmed, or disputed the 25/28-applicant, 98%-acceptance figure.

Further leads

• AEA RCT Registry, trial 17146 (https://www.socialscienceregistry.org/trials/17146) — both archive_page and WebFetch returned HTTP 500 to the direct URL this session; candidate addition to sources.md's known-blocked-routes list. Even if reachable, its listed investigators (Erica Chiang and Nikhil Garg) are the same author group, so it cannot itself serve as independent corroboration — its only value would be confirming pre-registration commitments against the paper's reported analysis.
• news.cornell.edu's article page (news.cornell.edu/stories/2026/08/research-helps-nyc-students-aim-higher-public-high-school-applications) returned HTTP 403 to archive_page this session — a new data point for the known-blocked-routes pattern; worth a Wayback-Machine-first retry to get a quotable, sha-bearing receipt for its "59% more likely to apply" figure.
• Peng, K. & Ryu, E., "Connecting application behavior to undermatching in New York City school choice," Nature Cities (2026), https://www.nature.com/articles/s44284-026-00486-0 — shares co-author Kenny Peng with the nudge RCT paper, so not independent per the author-group test, but is a different paper in a different (peer-reviewed journal) venue and may discuss the nudge deployment as related work; not read this session.
• Chalkbeat NYC, which has covered NYC high school admissions policy closely since at least 2015, was searched generically but not searched directly on its own site for this specific deployment; a dedicated site search (chalkbeat.org) is a cheap next step for a future session.
• The claim (via Cornell News, per WebSearch summary, itself unverified-quote) that the paper won a "Best Paper with Student Lead Author Award" at ACM EC — not checked against the EC26 awards page or program site this session.

Entity candidates

• Sean Corcoran — person — co-author of Corcoran et al. 2018, the earlier non-personalized "reach, match, safety" list intervention in NYC school choice that this paper explicitly measures its own design against and distinguishes itself from (the predecessor the paper's safety-constraint contribution is argued relative to, not a co-author of this paper).
• Chloe Corradini and Jennifer Idoux — persons/concept — authors of the NYCPS offer-likelihood tool (Corradini and Idoux 2025a) that this paper's deployment directly builds on and whose "High ≥ 99% chance" bucketing the paper's own congestion-safety threshold (L=0.95) is calibrated against; the most directly foundational predecessor system within NYCPS itself.
• Erica Chiang — person — lead author of the nudge RCT paper and the Siegel PiTech PhD Impact Fellow who designed and ran the deployment; not yet a vault entity (only entity-kenny-peng and entity-nikhil-garg exist among this paper's co-authors).
• Jon Kleinberg — person — co-author; prominent theorist of networks and algorithmic decision-making, not yet a vault entity.
• Eva Tardos — person — co-author; prominent algorithmic game theory / combinatorial optimization theorist, not yet a vault entity.