intake:algorithmic-public-systems:capital-fit | capital-fit | P2 | Establish funding and implementation fit for Algorithmic Public Systems. | Capital pathways: Digital infrastructure, public-interest technology, creator support, independent journalism, responsible investment, nonprofit capacity and research funding. Record instrument, payer, intermediary, recipient, restriction, duration, risk allocation, beneficiary reach and observed outcome separately. | Query the funding and award sources in the issue packet; distinguish appropriations, obligations, outlays, grants, contracts, loans, guarantees, tax expenditures and private capital. | Published · owner-authorized |
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intake:algorithmic-public-systems:definition-authority | definition-authority | P1 | Add authoritative definitions and scope boundaries for Algorithmic Public Systems. | research scope: Assess platforms, media, AI, data stewardship, public-figure boundaries, corporate influence, fiduciary and philanthropic governance, digital access and editorial independence. For Algorithmic Public Systems, treat the unit of analysis as a source-defined law, rule, institution, service, market, exposure, process or observed outcome. Do not infer a claim from category membership, identity, geography or association alone. Starting authorities: Privacy and Data Security, National Broadband Map Data Download, Tax-Exempt Organizations and Charitable Trust Statistics. | Extract the operative definition, jurisdiction, exclusions, legal/status hierarchy and date from the cited sources; store exact locators. | Published · owner-authorized |
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intake:algorithmic-public-systems:implementation | implementation | P2 | Document supported implementation pathways for Algorithmic Public Systems. | Implementation approach: Separate content, conduct, platform design, data use, actor role, economic incentives, governance authority and measurable outcomes. Operationally, create an issue-specific logic chain from authority and need through implementer, action, output, outcome, remedy and feedback. | Populate responsible authority, implementer, delivery channel, eligibility, process step, service standard, output, outcome, cost and failure mode. | Published · owner-authorized |
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intake:algorithmic-public-systems:measures-trends | measures-trends | P1 | Assign dated measures or a comparable time series for Algorithmic Public Systems. | Measurement framework: enrollment and access; cost, finance or aid; staffing and institutional capacity; completion, attainment or learning outcome; discipline, exclusion, complaint or remedy; gap by subgroup, institution and geography. Denominator: Eligible-age population, applicants, enrolled students or institutions, clearly separated by measure. Cadence: Academic-year and cohort trends; preserve changes in institution and student universes. | Extract a baseline, latest value and comparable time series for each feasible indicator; retain numerator, denominator, geography, subgroup, methodology and vintage. | Published · owner-authorized |
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intake:algorithmic-public-systems:relationships | relationships | P2 | Add explicit supported relationship records for Algorithmic Public Systems where evidence warrants them. | Candidate research relationships: Extractive Partnerships & Reputation-Washing (0.28; shared institutional/source pathway); Institutional Pragmatism (0.28; shared institutional/source pathway); Algorithmic Discrimination & Surveillance (0.25; algorithmic). These are routing hypotheses based on title/source proximity, not research intersectionality or causal findings. | For each candidate pair, test a named shared mechanism, direction, comparator, counterfactual, distinctiveness and independent source support. | Published · owner-authorized |
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intake:algorithmic-public-systems:risks-safeguards | risks-safeguards | P2 | Document issue-specific risks, failure conditions, and safeguards for Algorithmic Public Systems. | Material risks: Surveillance, algorithmic error, concentration, editorial capture, reputation harm, opaque influence, mission drift, data misuse and access inequality. Required safeguards: Auditability, privacy, appeals, editorial independence, conflict disclosure, fiduciary process, user notice, accessibility and independent evaluation. | Create one risk-control record per material risk with trigger, affected population, preventive control, detective control, remedy, owner and monitoring indicator. | Published · owner-authorized |
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intake:algorithmic-public-systems:rulings-confirmatory | rulings-confirmatory | P1 | Acquire confirmatory rulings for Algorithmic Public Systems. | Carpenter v. United States, 585 U.S. 296 (U.S. Supreme Court, 2018). Access to historical cell-site location information generally requires a warrant despite third-party possession. Carpenter v. United States supports or supplies a protective rule relevant to Algorithmic Public Systems. Fit tier: Strong analogue. The placement must be used only within the holding and limitations recorded here. | Verify official text, current precedential status, later treatment, pinpoint holding, jurisdiction and exact issue-claim linkage; add lower-court or agency authorities where needed. | Published · owner-authorized |
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intake:algorithmic-public-systems:rulings-disconfirmatory | rulings-disconfirmatory | P1 | Acquire disconfirmatory, limiting, adverse, or narrowing rulings for Algorithmic Public Systems. | Washington v. Davis, 426 U.S. 229 (U.S. Supreme Court, 1976). A racially disproportionate impact, without proof of discriminatory purpose, does not by itself establish a constitutional equal-protection violation. Washington v. Davis rejects, narrows, limits, or supplies adverse authority relevant to Algorithmic Public Systems. Fit tier: Strong analogue. The placement must be used only within the holding and limitations recorded here. | Verify official text, current precedential status, later treatment, pinpoint holding, jurisdiction and exact issue-claim linkage; add lower-court or agency authorities where needed. | Published · owner-authorized |
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intake:algorithmic-public-systems:stakeholder-authority | stakeholder-authority | P2 | Identify documented affected groups, institutional actors, and decision authority for Algorithmic Public Systems. | Stakeholder set: Users, creators, journalists, researchers, platforms, public figures and private civilians, investors, boards, donors, nonprofits, regulators and affected communities. Separate affected people, rights holders, duty bearers, funders, implementers, data holders, adjudicators, advocates and potential opposing interests. | Validate each stakeholder class from a cited source; record authority, interest, exposure, decision rights, accountability and conflict-of-interest. | Published · owner-authorized |
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intake:algorithmic-public-systems:systems-differences | systems-differences | P2 | Source the systems and distributional differences material to Algorithmic Public Systems. | Systems map: Platform governance and competition; privacy and data law; AI risk management; media and defamation; corporate governance; philanthropy and procurement; digital infrastructure. Compare legal regime, eligibility, administrative process, funding, delivery channel, data definition, geography and population before making cross-system claims. | Build a comparison matrix across jurisdictions and subgroups; record which dimensions are comparable, non-comparable or missing. | Published · owner-authorized |
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intake:algorithmic-public-systems:values-responsibilities | values-responsibilities | P2 | Document issue-specific values and institutional responsibilities for Algorithmic Public Systems. | Values and responsibilities: Expression, privacy, access, accuracy, independence, stewardship, competition and accountable innovation. Translate these into explicit duties for government, institutions, funders, implementers, data stewards and affected-community governance. | Link each asserted value to a legal, policy or ethical authority and to a measurable institutional responsibility, safeguard and remedy. | Published · owner-authorized |
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