{"built": "2026-10-07T00:00:26Z", "tool_version": "0.1.0", "studies": [{"slug": "affective-polarization-interventions", "title": "Testing 33 Student-Designed Interventions to Reduce Affective Polarization", "source": "submitted", "repo_url": "https://github.com/yrvelez/affective-polarization-interventions", "folder_url": "https://github.com/yrvelez/affective-polarization-interventions/tree/main", "report_url": "https://github.com/yrvelez/affective-polarization-interventions/blob/main/report.md", "data_url": "https://github.com/yrvelez/affective-polarization-interventions/tree/main/extensions", "study_json_url": "https://github.com/yrvelez/affective-polarization-interventions/blob/main/study.json", "release_status": "draft", "registration_status": "none", "extensions": [{"id": "generalizability_conditional", "kind": "boundary", "label": "Generalizability", "brief_id": "generalizability_conditional", "mode": "survey_experiment", "title": "Does the Perception gap effect hold across partisanship and baseline polarization?", "hypothesis": "The video lowers affective polarization relative to control, more among strong partisans and the high baseline tercile; the effect is smaller or null among pure independents.", "why": "Addresses the sample failure: independents were dropped and no moderator was examined.", "debate": null, "status": "proposed", "qsf": "extensions/generalizability_conditional.qsf", "svg": "extensions/generalizability_conditional.svg", "text": "extensions/generalizability_conditional.txt", "addresses": ["U2"]}, {"id": "theoretical_debate", "kind": "observational", "label": "Theoretical debate", "brief_id": "theoretical_debate", "mode": "survey_experiment", "title": "Perception-Gap Correction vs. Superordinate Identity: A 2\u00d72 Factorial Test of Additive vs. Substitutive Mechanisms for Reducing Affective Polarization", "hypothesis": "The perception-gap correction and superordinate-identity interventions have substitutive (negative interaction) effects on affective polarization, indicating they operate through a shared psychological mechanism rather than independent channels.", "why": "It fixes nothing yet. A debate brief needs a new literature search on correcting misperceptions versus superordinate identity or empathy.", "debate": null, "status": "proposed", "qsf": null, "svg": "extensions/theoretical_debate.svg", "text": "extensions/theoretical_debate.txt", "addresses": []}, {"id": "data_to_collect", "kind": "observational", "label": "Data to collect", "brief_id": "data_to_collect", "mode": "data_collection", "title": "Sample-flow and attrition audit from existing data", "hypothesis": "Attrition from 6,086 to 3,825 is balanced across arms, and the Perception gap coefficient of \u22122.3 points (95% CI [\u22124.3, \u22120.4]) remains statistically distinguishable from zero under inverse-probability weighting for differential attrition.", "why": "Addresses the sample failure and U2 at no fielding cost.", "debate": null, "status": "proposed", "qsf": null, "svg": "extensions/data_to_collect.svg", "text": "extensions/data_to_collect.txt", "addresses": ["U2"]}], "sample_kind": "human", "causal": true, "design_diagram": "figures/design.svg", "design_diagram_text": "figures/design.txt", "review_claims": {"supported": 12, "total": 12, "open_analytical": 8, "on_revised_text": true}, "review_rounds": 1, "potential": {"verdict": "Run one follow-up, and run the preregistered replication of the Perception gap video first. The current evidence is one uncorrected hit (-2.3, p=0.019) among 64 tests, most arms are underpowered, and H2 shows nothing. Run the zero-cost attrition audit first, since it determines whether the existing contrasts can be trusted. The retrieved literature is off-topic, so no genuine debate can be named; the theoretical brief is a placeholder pending a proper search. The moderator study on party and baseline polarization only makes sense if the replication holds.", "causes": ["power", "analysis", "design", "sample", "measurement"], "debates": [], "briefs": [{"id": "advance_design", "mode": "survey_experiment", "title": "Preregistered, powered replication of the Perception gap video with a manipulated mechanism", "needs_qsf": true}, {"id": "theoretical_debate", "mode": "survey_experiment", "title": "No debate visible in the retrieved works", "needs_qsf": false}, {"id": "generalizability_conditional", "mode": "survey_experiment", "title": "Does the Perception gap effect hold across partisanship and baseline polarization?", "needs_qsf": true}, {"id": "data_to_collect", "mode": "data_collection", "title": "Sample-flow and attrition audit from existing data", "needs_qsf": false}]}, "registration_url": null, "provenance_mode": "fully_agentic", "reviewer_pass": true, "cost_usd": 0.822, "tokens_in": 418465, "tokens_out": 57822, "design_type": "survey_experiment", "population": "online_panel", "country": "US", "constructs": ["affective_polarization", "democratic_norms", "persuasion"], "keywords": ["affective polarization", "feeling thermometer", "undemocratic practices", "interventions", "multi-arm"], "n": 4547, "created": "2026-10-03", "hypotheses": {"registered": 0, "unregistered": 66, "deviation": 0, "robustness": 0, "exploratory": 0, "supported": 2}, "synthetic": false, "authors": ["Yamil Velez"], "kind": "filedrawer_package", "files": {"data": ["extensions/generalizability_conditional.qsf", "survey.qsf"], "scripts": ["original/replication_script.R", "run.sh", "scripts/01_tidy.py", "scripts/02_clean.py", "scripts/03_registered.py", "scripts/04_debug.py"], "docs": ["README.md", "RUN.md", "codebook.md", "extensions/data_to_collect.txt", "extensions/generalizability_conditional.txt", "extensions/theoretical_debate.txt", "figures/design.txt", "pap.md", "report.md", "review.md"], "n_files": 63, "n_data": 2, "n_scripts": 6, "data_formats": ["Qualtrics schema"], "languages": ["Python", "R", "shell"]}, "readme_excerpt": "Testing 33 Student-Designed Interventions to Reduce Affective Polarization Yamil Velez. Adaptive online survey experiment (Lucid Theorem, November 9\u201325, 2022; Columbia IRB AAAU3946) testing 33 interventions designed by students in a Fall 2022 Experimental Design course against a pure control, among partisans. Interventions include videos (e.g. a perception-gap video, a Bren\u00e9 Brown empathy video), articles (e.g. a meta-dehumanization correction, bipartisan legislation examples), images and interactive quizzes; the report lists each one. One intervention, a perception-gap video, significantly re", "summary": "This study asks whether short, student-designed interventions can reduce affective polarization or support for undemocratic practices among US adults. In an online survey experiment, 32 interventions (videos, articles, images, quizzes) were each compared with a pure control; a 33rd arm, a GPT-3 chatbot, was excluded after technical failures. Of 6,086 raw respondents, 4,547 passed the sample filters, and the models use fewer because of missing data. One intervention was nominally significant for affective polarization: the Perception gap video lowered the post-treatment thermometer-based measur", "extraction": {"method": "study_json", "llm_used": false, "warnings": [], "bytes_read": 122582}, "record_url": "/records/7f0f760039f9.json", "harvested_at": "2026-10-04T20:54:41Z", "id": "7f0f760039f9", "paper_url": "/records/7f0f760039f9/paper.md", "badges": [{"name": "provenance", "label": "provenance", "value": "fully agentic", "color": "indigo", "href": null}, {"name": "review", "label": "review", "value": "12/12 claims supported", "color": "green", "href": null}, {"name": "registration", "label": "plan", "value": "reconstructed", "color": "amber", "href": null}, {"name": "release", "label": "status", "value": "draft", "color": "amber", "href": null}, {"name": "design", "label": "design", "value": "survey experiment", "color": "slate", "href": null}, {"name": "data", "label": "data", "value": "open data", "color": "green", "href": null}, {"name": "cost", "label": "model calls", "value": "$0.82", "color": "slate", "href": null}], "paper_base": "/records/7f0f760039f9/", "paper_source": "report.md", "readme_url": "https://github.com/yrvelez/affective-polarization-interventions/blob/main/README.md"}, {"slug": "ai-discernment", "title": "Improving AI Discernment: do misinformation interventions help people spot AI-generated media?", "source": "submitted", "repo_url": "https://github.com/yrvelez/ai-discernment", "folder_url": "https://github.com/yrvelez/ai-discernment/tree/main", "report_url": "https://github.com/yrvelez/ai-discernment/blob/main/report.md", "data_url": "https://github.com/yrvelez/ai-discernment/tree/main/data", "study_json_url": "https://github.com/yrvelez/ai-discernment/blob/main/study.json", "release_status": "released", "registration_status": "registered", "extensions": [{"id": "advance_design", "kind": "mechanism", "label": "Design advance", "brief_id": "advance_design", "mode": "survey_experiment", "title": "Discernment vs response bias in AI labels", "hypothesis": "Automated Flagging raises d' relative to control; any increase in AI responses (a more liberal criterion) is separate from sensitivity, and the stated error rate reduces reliance on labels.", "why": "Fixes measurement: computes sensitivity and criterion from AI and real post responses, and adds confidence ratings.", "debate": "D1", "status": "proposed", "qsf": "extensions/advance_design.qsf", "svg": "extensions/advance_design.svg", "text": "extensions/advance_design.txt", "addresses": ["U1"]}, {"id": "generalizability_conditional", "kind": "boundary", "label": "Generalizability", "brief_id": "generalizability_conditional", "mode": "survey_experiment", "title": "Label effect by label reliability and prior AI skill", "hypothesis": "Labels at 95% accuracy raise accuracy over control; labels at 70% accuracy raise it less or not at all, and may lower accuracy on posts the labels mislabel. The benefit is larger for low-familiarity respondents.", "why": "Fixes the sample and missing-moderator causes by testing where the one robust effect holds.", "debate": null, "status": "proposed", "qsf": "extensions/generalizability_conditional.qsf", "svg": "extensions/generalizability_conditional.svg", "text": "extensions/generalizability_conditional.txt", "addresses": []}, {"id": "theoretical_debate", "kind": "alternative", "label": "Theoretical debate", "brief_id": "theoretical_debate", "mode": "survey_experiment", "title": "Platform labels vs user training head to head", "hypothesis": "H1 (s-frame): Automated Flagging yields higher total accuracy than the AI Literacy Guide. H2: the combined arm exceeds either single arm (additive effects). Rival (i-frame): the Guide matches labels, particularly at 1 week.", "why": "Fixes design and power: contrasts only the best-supported system arm with the best user-side arm in a focused comparison.", "debate": "D1", "status": "proposed", "qsf": "extensions/theoretical_debate.qsf", "svg": "extensions/theoretical_debate.svg", "text": "extensions/theoretical_debate.txt", "addresses": ["U1"]}], "sample_kind": "human", "causal": true, "design_diagram": "figures/design.svg", "design_diagram_text": "figures/design.txt", "review_claims": {"supported": 9, "total": 11, "open_analytical": 1, "on_revised_text": true}, "review_rounds": 1, "potential": {"verdict": "A follow-up is worth running, but narrowly. The only robust result is Automated Flagging (+3.2 to +4.4 points), while the other nominal effects vanish without covariates. Run the advance_design brief first: it tests whether the label effect is real discernment or a shift toward answering 'AI', and it is sized to the observed effect. The head-to-head debate brief should follow. The literature retrieved is thin on AI-media detection, so the i-frame vs s-frame debate rests on one contesting source (Chater & Loewenstein) and should be treated as framing, not established disagreement.", "causes": ["power", "analysis", "measurement", "design"], "debates": ["Individual-level vs system-level fixes"], "briefs": [{"id": "advance_design", "mode": "survey_experiment", "title": "Discernment vs response bias in AI labels", "needs_qsf": true}, {"id": "theoretical_debate", "mode": "survey_experiment", "title": "Platform labels vs user training head to head", "needs_qsf": true}, {"id": "generalizability_conditional", "mode": "survey_experiment", "title": "Label effect by label reliability and prior AI skill", "needs_qsf": true}]}, "registration_url": "https://aspredicted.org/q2eh95.pdf", "provenance_mode": "fully_agentic", "reviewer_pass": true, "cost_usd": 1.321, "tokens_in": 605205, "tokens_out": 110157, "design_type": "survey_experiment", "population": "online_panel", "country": "United States", "constructs": ["ai_generated_content", "media_trust", "misinformation", "persuasion", "survey_methodology"], "keywords": ["AI discernment", "deepfakes", "synthetic media", "inoculation", "accuracy nudge", "AI literacy", "misinformation interventions", "Lin estimator"], "n": 2030, "created": "2026-10-04", "hypotheses": {"registered": 48, "unregistered": 0, "deviation": 0, "robustness": 24, "exploratory": 24, "supported": 16}, "synthetic": false, "authors": ["Yamil Velez"], "kind": "filedrawer_package", "files": {"data": ["data/clean.csv", "data/raw_tidy.csv", "extensions/advance_design.qsf", "extensions/generalizability_conditional.qsf", "extensions/theoretical_debate.qsf", "inputs/replication_data.csv", "inputs/survey.qsf", "survey.qsf"], "scripts": ["original/code/create_replication_data.R", "original/code/replication_script.R", "run.sh", "scripts/01_tidy.py", "scripts/02_clean.py", "scripts/03_registered.py", "scripts/04_debug.py"], "docs": ["AGENTS.md", "README.md", "RUN.md", "codebook.md", "extensions/advance_design.txt", "extensions/generalizability_conditional.txt", "extensions/theoretical_debate.txt", "figures/design.txt", "inputs/pap.md", "original/site/index.html", "pap.md", "report.md", "review.md"], "n_files": 93, "n_data": 8, "n_scripts": 7, "data_formats": ["Qualtrics schema", "csv"], "languages": ["Python", "R", "shell"]}, "readme_excerpt": "Improving AI Discernment Survey experiment testing whether interventions from the misinformation literature improve people's ability to tell AI-generated images and videos from authentic news media. Twelve arms (control plus flagging, provenance, automated flagging, accuracy nudge, breathing, mindfulness, inoculation, three AI-literacy materials and an AI-text video) on a simulated social feed (\"FutureFeed\"); U.S. adults via CloudResearch, fielded 30 October to 21 November 2023; N = 2,030 analyzed. Columbia University IRB AAAU9484. This repository is a study package in the filedrawer layout. P", "summary": "Can brief misinformation interventions help people tell AI-generated media from authentic media? We ran a registered online survey experiment in the United States (fielded October to November 2023). Of 2,257 respondents collected, 2,030 were analysed; they were randomised to a control (n=181) or one of 11 interventions, then judged posts in a simulated feed. Pooled across arms, total accuracy was not distinguishable from control (+1.0 point, 95% CI [\u22120.3, +2.4]). Automated Flagging raised accuracy by 4.4 points (95% CI [1.4, 7.3]). The AI Literacy Guide (+4.7 points) and Mindfulness (\u22123.9 poin", "extraction": {"method": "study_json", "llm_used": false, "warnings": [], "bytes_read": 157990, "files_read": [{"path": "study.json", "bytes": 60649}, {"path": "report.md", "bytes": 59663}, {"path": "pap.json", "bytes": 22933}, {"path": "provenance/provenance.json", "bytes": 11465}, {"path": "README.md", "bytes": 3280}]}, "record_url": "/records/59ed1f62eb8a.json", "harvested_at": "2026-10-05T16:00:43Z", "id": "59ed1f62eb8a", "paper_url": "/records/59ed1f62eb8a/paper.md", "badges": [{"name": "provenance", "label": "provenance", "value": "fully agentic", "color": "indigo", "href": null}, {"name": "review", "label": "review", "value": "9/11 claims supported", "color": "amber", "href": null}, {"name": "registration", "label": "plan", "value": "pre-registered #151,281", "color": "green", "href": "https://aspredicted.org/q2eh95.pdf"}, {"name": "release", "label": "status", "value": "released", "color": "green", "href": null}, {"name": "doi", "label": "DOI", "value": "10.5281/zenodo.23166168", "color": "blue", "href": "https://doi.org/10.5281/zenodo.23166168"}, {"name": "design", "label": "design", "value": "survey experiment", "color": "slate", "href": null}, {"name": "data", "label": "data", "value": "open data", "color": "green", "href": null}, {"name": "cost", "label": "model calls", "value": "$1.32", "color": "slate", "href": null}], "paper_base": "https://raw.githubusercontent.com/yrvelez/ai-discernment/main/", "paper_source": "report.md", "readme_url": "https://github.com/yrvelez/ai-discernment/blob/main/README.md"}, {"slug": "dialogue-through-disagreement", "title": "Dialogue Through Disagreement? A Chatbot as a Measurement Device for Persuasion", "source": "uploaded", "repo_url": null, "folder_url": null, "report_url": "/records/f4e10027a301/files/report.md", "data_url": null, "study_json_url": "/records/f4e10027a301/files/study.json", "release_status": "released", "registration_status": "none", "extensions": [], "sample_kind": "human", "causal": true, "design_diagram": "figures/design.svg", "design_diagram_text": "figures/design.txt", "review_claims": null, "review_rounds": 0, "potential": null, "registration_url": null, "provenance_mode": "fully_agentic", "reviewer_pass": false, "cost_usd": 0.074, "tokens_in": 34783, "tokens_out": 12723, "design_type": "survey_experiment", "population": "online_panel", "country": "US", "constructs": ["ai_generated_content", "deliberation", "persuasion", "policy_support"], "keywords": ["chatbot", "op-ed", "persuasion", "infrastructure spending", "certainty"], "n": 446, "created": "2026-10-05", "hypotheses": {"registered": 0, "unregistered": 4, "deviation": 0, "robustness": 0, "exploratory": 3, "supported": 4}, "synthetic": false, "authors": ["Yamil R. Velez", "Patrick Liu"], "kind": "filedrawer_package", "files": {"data": [], "scripts": ["scripts/01_tidy.py", "scripts/02_clean.py", "scripts/03_registered.py", "scripts/04_exploratory.py"], "docs": ["RUN.md", "codebook.md", "figures/design.txt", "pap.md", "report.md"], "n_files": 39, "n_data": 0, "n_scripts": 4, "data_formats": [], "languages": ["Python"]}, "summary": "Can a chatbot conversation serve as a measurement device for persuasion after people read an argument against their own position? In a US online-panel survey experiment (449 recruited, 446 analysed), participants read either an op-ed opposing their position or an unrelated placebo article, then talked with a chatbot. Compared with placebo, the op-ed raised support for the op-ed position by 0.64 scale points (95% CI [0.37, 0.91]) and lowered certainty by 5.3 points (95% CI [\u22128.9, \u22121.7]). Participants in the op-ed arm were also more likely to use counterargument keywords (+16.6 points, 95% CI [9", "extraction": {"method": "study_json", "llm_used": false, "warnings": [], "bytes_read": 38956}, "record_url": "/records/f4e10027a301.json", "harvested_at": "2026-10-05T20:47:09Z", "id": "f4e10027a301", "paper_url": "/records/f4e10027a301/paper.md", "badges": [{"name": "provenance", "label": "provenance", "value": "fully agentic", "color": "indigo", "href": null}, {"name": "review", "label": "review", "value": "unreviewed", "color": "slate", "href": null}, {"name": "registration", "label": "plan", "value": "reconstructed", "color": "amber", "href": null}, {"name": "release", "label": "status", "value": "released", "color": "green", "href": null}, {"name": "design", "label": "design", "value": "survey experiment", "color": "slate", "href": null}, {"name": "data", "label": "data", "value": "report only", "color": "slate", "href": null}, {"name": "cost", "label": "model calls", "value": "$0.07", "color": "slate", "href": null}], "paper_base": "/records/f4e10027a301/", "paper_source": "report.md", "files_url": "/records/f4e10027a301/files/", "submission": {"method": "package_upload", "n_files": 39, "server_processing": "allowlist and leak scan only; no model call, no network fetch", "acknowledged": [], "tool_version": "0.1.0", "commit": "19b5c9edf162", "modified": false, "source_url": "https://github.com/yrvelez/filedrawer/tree/19b5c9edf1623c4c00cc31010b8e38594c57b", "selfcheck_passed": true}}, {"slug": "fact-networks-conjoint", "title": "Who Shares What From Whom: An Ego-Network Conjoint of Misinformation Sharing", "source": "submitted", "repo_url": "https://github.com/yrvelez/fact-networks-conjoint", "folder_url": "https://github.com/yrvelez/fact-networks-conjoint/tree/main", "report_url": "https://github.com/yrvelez/fact-networks-conjoint/blob/main/report.md", "data_url": "https://github.com/yrvelez/fact-networks-conjoint/tree/main/extensions", "study_json_url": "https://github.com/yrvelez/fact-networks-conjoint/blob/main/study.json", "release_status": "draft", "registration_status": "registered", "extensions": [{"id": "advance_design", "kind": "mechanism", "label": "Design advance", "brief_id": "advance_design", "mode": "survey_experiment", "title": "Randomized sharer profiles for trust and closeness", "hypothesis": "Randomly assigned close and high-trust sharers raise sharing likelihood relative to distant and low-trust sharers (about 3 points), with effects possibly differing by information type.", "why": "Fixes the design cause: randomizing trust and tie removes confounding with real contact traits, and an opt-out answers U1.", "debate": null, "status": "proposed", "qsf": "extensions/advance_design.qsf", "svg": "extensions/advance_design.svg", "text": "extensions/advance_design.txt", "addresses": ["U1"]}, {"id": "generalizability_conditional", "kind": "boundary", "label": "Generalizability", "brief_id": "generalizability_conditional", "mode": "survey_experiment", "title": "Fact-check effect by topic", "hypothesis": "The fact-check sharing penalty relative to true/neutral posts is smaller for health than for political items. Misinformation is shared less than true content in both topics. Exploratory: the topic difference varies by survey language.", "why": "Fixes the sample cause: randomizing topic with adequate health items answers U3.", "debate": null, "status": "proposed", "qsf": "extensions/generalizability_conditional.qsf", "svg": "extensions/generalizability_conditional.svg", "text": "extensions/generalizability_conditional.txt", "addresses": ["U3"]}, {"id": "theoretical_debate", "kind": "alternative", "label": "Theoretical debate", "brief_id": "theoretical_debate", "mode": "survey_experiment", "title": "Absolute versus relative misinformation sharing by belief", "hypothesis": "In forced choice, belief in disputed claims predicts more misinformation and less true-content choice (mirrored). In single-item format, if the belief slope for misinformation sharing remains positive, the effect is absolute; if it is near zero while the true-content slope is negative or null, it is purely relative.", "why": "Fixes the measurement cause: single-item rating with an opt-out breaks the forced-choice link and answers U2.", "debate": null, "status": "proposed", "qsf": "extensions/theoretical_debate.qsf", "svg": "extensions/theoretical_debate.svg", "text": "extensions/theoretical_debate.txt", "addresses": ["U2"]}], "sample_kind": "human", "causal": true, "design_diagram": "figures/design.svg", "design_diagram_text": "figures/design.txt", "review_claims": {"supported": 11, "total": 12, "open_analytical": 11, "on_revised_text": true}, "review_rounds": 1, "potential": {"verdict": "No genuine debate is visible in the retrieved works, which are off-topic (COVID, AI, behavioural policy), so the debates list is empty and the theoretical-debate brief is built on the editor's open question U2 rather than the literature. A follow-up is worth running because the one robust result, the information-type penalty, is already settled, while the interesting claims about trust, closeness and belief-driven sharing rest on measured traits or a forced-choice artefact. Run the randomized-sharer brief first: it resolves U1, the largest threat to the study's contribution, and it is cheap to field within a single wave. The format and topic experiments can follow..", "causes": ["design", "power", "measurement", "sample", "analysis"], "debates": [], "briefs": [{"id": "advance_design", "mode": "survey_experiment", "title": "Randomized sharer profiles for trust and closeness", "needs_qsf": true}, {"id": "theoretical_debate", "mode": "survey_experiment", "title": "Absolute versus relative misinformation sharing by belief", "needs_qsf": true}, {"id": "generalizability_conditional", "mode": "survey_experiment", "title": "Fact-check effect by topic", "needs_qsf": true}]}, "registration_url": "https://aspredicted.org/8njc-mpqw.pdf", "provenance_mode": "fully_agentic", "reviewer_pass": true, "cost_usd": 2.287, "tokens_in": 884814, "tokens_out": 54052, "design_type": "conjoint", "population": "yougov", "country": "US", "constructs": ["conjoint_preferences", "fact_checking", "media_trust", "misinformation", "partisanship", "social_norms"], "keywords": ["misinformation", "sharing", "conjoint", "ego network", "social networks", "fact-check", "source credibility", "Latino"], "n": 12000, "created": "2026-10-03", "hypotheses": {"registered": 40, "unregistered": 0, "deviation": 0, "robustness": 0, "exploratory": 17, "supported": 22}, "synthetic": false, "authors": ["Yamil R. Velez"], "kind": "filedrawer_package", "files": {"data": ["extensions/advance_design.qsf", "extensions/generalizability_conditional.qsf", "extensions/theoretical_debate.qsf", "inputs/survey.qsf", "survey.qsf"], "scripts": ["inputs/build_pap.py", "inputs/build_qsf.py", "inputs/prepare_export.R", "run.sh", "scripts/01_tidy.py", "scripts/02_clean.py", "scripts/03_registered.py", "scripts/04_exploratory.py", "scripts/dbg.py"], "docs": ["AGENTS.md", "README.md", "RUN.md", "codebook.md", "extensions/advance_design.txt", "extensions/generalizability_conditional.txt", "extensions/theoretical_debate.txt", "figures/design.txt", "inputs/pap.md", "pap.md", "report.md", "review.md"], "n_files": 193, "n_data": 5, "n_scripts": 9, "data_formats": ["Qualtrics schema"], "languages": ["Python", "R", "shell"]}, "readme_excerpt": "Who Shares What From Whom: An Ego-Network Conjoint of Misinformation Sharing Study repository in the filedrawer layout. The package (report, tidy data, scripts, results, provenance) is generated at the root of this repository by the pipeline; the inputs live in inputs/. Depositing with an AI coding agent, or by hand: follow AGENTS.md in this repository. It says what must never be committed (the raw export), how to strip identifiers, and what the models see. Inputs (inputs/) export.csv Qualtrics export (raw; gitignored, it contains identifiers) survey.qsf survey schema pap.md pre-analysis plan ", "summary": "We ask how the type of information and the characteristics of the person who shares it relate to whether people choose to share a post and whether they judge it plausible. In a registered conjoint experiment with 12,000 YouGov profile ratings in the US, headlines (true or neutral, false, or a fact-check) were randomly attributed to real contacts named by respondents, whose traits were measured. Misinformation was chosen for sharing 18.2 points less often than true or neutral content (95% CI [\u221220.4, \u221216.0]); fact-checks were shared 13.3 points less overall, driven by political items. 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