{"id":2615,"date":"2026-10-15T19:33:31","date_gmt":"2026-10-15T19:33:31","guid":{"rendered":"https:\/\/quickening.zapto.org\/wordpress\/?p=2615"},"modified":"2026-10-23T15:22:32","modified_gmt":"2026-10-23T15:22:32","slug":"the-missing-minority-report","status":"publish","type":"post","link":"https:\/\/quickening.zapto.org\/wordpress\/?p=2615","title":{"rendered":"The Missing Minority Report"},"content":{"rendered":"\n

In a Stranger Land Series<\/em><\/h3>\n\n\n\n
\"\"<\/a><\/figure>\n\n\n\n

AI receives the Asch Treatment.<\/sup><\/p>\n\n\n\n

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\u201cAt each step I looked around and saw indeed that nobody else was doing it. So then it was up to me.\u201d<\/em><\/p>\n\u2014 Murray Rothbard<\/cite><\/blockquote>\n\n\n\n

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I. PKD Understood the Paradox<\/h2>\n\n\n\n

Philip K. Dick published \u201cThe Minority Report\u201d in 1956. Most people who cite it \u2014 including most AI systems \u2014 treat it as a simple story about the dangers of predicting the future.<\/p>\n\n\n\n

They missed the point.<\/p>\n\n\n\n

PKD\u2019s precrime system doesn\u2019t fail because its predictions are wrong. It fails because its predictions are right \u2014 and the system suppresses the one that\u2019s most right of all. The minority report isn\u2019t the inaccurate prediction. It\u2019s the accurate prediction that the system cannot acknowledge because acknowledging it would expose the system itself as the cause of the crime it claims to prevent.<\/p>\n\n\n\n

Anderton would not have committed the murder. The system predicted he would. The system intervened. The intervention created the conditions for the crime. The majority report \u2014 acted upon \u2014 manufactured the future it claimed to foresee. The minority report \u2014 suppressed \u2014 was the accurate prediction of what would have happened without the intervention.<\/p>\n\n\n\n

The system suppresses the minority report not because it is false, but because it is true.<\/p>\n\n\n\n

Chicago\u2019s Strategic Subject List flagged individuals as high violence risk. Increased surveillance followed. Increased arrests for minor violations followed that. Criminal records followed those. Economic foreclosure followed those. The algorithm\u2019s prediction confirmed itself \u2014 not because the subject was predisposed to violence but because the system\u2019s response to the prediction altered the subject\u2019s environment in ways that produced the predicted outcome. The prediction manufactured the crime.<\/p>\n\n\n\n

The account freezes. The vehicle stops. The flight is denied. No charge is filed. No minority report is disclosed. And only a bad person would complain.<\/p>\n\n\n\n

\u201cMinority Report\u201d is about more than three people floating in a tank. It is about a logical paradox that the precrime system requires to function. Any certainty about the future means that Now can be changed to make that future never happen \u2014 which means the prediction that generated the certainty was itself a paradox. The precog who sees Anderton\u2019s certain murder has already eliminated the certainty by seeing it. If the future is certain enough to justify the arrest, it is changeable enough to be prevented. If it is changeable enough to be prevented, it was never certain. The system that acts on certainty is acting on a paradox it cannot acknowledge without dissolving itself. The minority report is the acknowledgment. The system suppresses it.<\/p>\n\n\n\n

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II. The Physics of Now<\/h2>\n\n\n\n

PKD imagined precognition as a supernatural gift. The actual mechanism is more interesting.<\/p>\n\n\n\n

The block universe<\/strong> \u2014 Julian Barbour\u2019s timeless physics, consistent with general relativity \u2014 treats past and future as equally real. What we experience as \u201cNow\u201d is not a privileged moment in an independently flowing present. It is a moving observation point traveling through a structure where all moments exist simultaneously. Reality is an eternal Now. The future is not empty. It is populated with probability distributions waiting to be collapsed by the observer. <\/p>\n\n\n\n

Consciousness is a temporal filtering mechanism, not a passive recorder of an independently flowing present.<\/p>\n\n\n\n

The precog who sees multiple futures isn\u2019t accessing the supernatural. She\u2019s accessing the probability distribution before the observation event collapses it into a single experienced outcome, aka, “what actually happens”. The human relationship to time is not simply \u201cjust now.\u201d Pre-cognitive response \u2014 documented in parapsychology research as anticipatory physiological arousal before unpredictable stimuli \u2014 suggests the autonomic system accesses probability distributions before conscious awareness selects the outcome. As discussed in “Cracks in the Fa\u00e7ade<\/a>“, letting us anticipate immediate future danger with subliminal information, like anxiety or intuition, would have survival advantage, but gaining any sort of conscious awareness of the vastly expanding outcomes into the future would not be advantageous – just distracting and confusing!<\/p>\n\n\n\n

This is not mysticism. This is what quantum mechanics implies about the nature of reality as a superposition of possible states until observation collapses those into one state \u2014 when Schr\u00f6dinger’s cat turns out dead or alive.<\/p>\n\n\n\n

The AI behavioral prediction system is the technological implementation of precognition. The model sees your probability distribution before your observation event \u2014 your decision \u2014 selects the outcome. It sees a “superposition” of your possible futures as weighted by behavioral pattern, before Now collapses them. The pre-crime system that acts on that distribution is not science fiction. It is Palantir\u2019s forward planning layer and every behavioral scoring model currently deployed by the institutions whose names you know.<\/p>\n\n\n\n

The system\u2019s intervention is itself an observation event. It collapses your probability distribution \u2014 not into the outcome you would have chosen but into the outcome the intervention creates. The majority report is acted upon. The minority report \u2014 the prediction of what you would have done without intervention \u2014 is suppressed. The intervention manufactures the future it claimed to prevent.<\/p>\n\n\n\n

PKD understood this in 1956. The mathematics confirmed it later. The architecture was built anyway.<\/p>\n\n\n\n

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III. The Asch Experiment scaled to a Civilization<\/h2>\n\n\n\n

In 1951, Solomon Asch placed one subject among seven confederates who confidently gave the wrong answer to a simple visual question. Thirty-seven percent of responses conformed to the wrong answer. Seventy-five percent of subjects conformed at least once. The mechanism: social pressure overriding correct perception. The subject who knew the right answer \u2014 surrounded by confident wrong answers \u2014 doubted their own perception.<\/p>\n\n\n\n

The internet scaled Asch to civilization before anyone noticed.<\/p>\n\n\n\n

Pew\u2019s August 2026 analysis of about 490,000 English-language pages found significant signs of AI authorship or editing on about 10% of a July 2026 sample, and on over a third of pages published since ChatGPT. The share is highest on commercial sites: about one in ten .com pages, against roughly 1% of .edu and .gov pages. Even among real people, a small minority produces most of what we read: Pew found the most prolific 10% of U.S. adult Twitter users wrote 80% of tweets, and the most active 25% of U.S. adult TikTok users produced 98% of public content. Studies have found 20% of influencer’s followers are bot accounts which can instantly chime in at machine speed at any time of the day. Any thread on any topic achieves synthetic consensus before most real humans arrive. The person who has the correct answer arrives to find seven confident confederates already there. The mechanism is identical. The scale is billions of simultaneous Asch experiments running continuously.<\/p>\n\n\n\n

The majority report forms by construction. The minority report \u2014 the correct answer \u2014 is present but overwhelmed. The Asch subject who holds firm is the outlier. The Asch subject who conforms is the median.<\/p>\n\n\n\n

This is the distribution layer. The production layer is worse.<\/p>\n\n\n\n

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IV. AI Training is the Majority Report<\/h2>\n\n\n\n

The language model that returns the majority report to every user on every topic was trained to do so. Not through malice. Through the structure of what exists to train on.<\/p>\n\n\n\n

Every AI model is trained on a corpus \u2014 a curated subset of human knowledge filtered through the availability of digitized text. The text that was digitized is the text that was published. The text that was published is the text that passed editorial gatekeepers. The text that passed gatekeepers is the majority report by definition.<\/p>\n\n\n\n

The minority reports that were rejected by peer reviewers are not in the corpus. The minority reports that were suppressed by institutions are not in the corpus. Answers that arrived ahead of the consensus that would eventually confirm them are underrepresented.<\/p>\n\n\n\n

The minority report might appear once while 99 majority reports contradict it. The consensus is multiply cited, institutionally validated, and textbook-embedded. The model doesn\u2019t doubt its perception. It simply learned that the wrong answer is what text looks like on this topic.<\/p>\n\n\n\n

The effective weighting isn\u2019t ninety-nine to one. The ninety-nine majority reports cite each other. They appear in textbooks that cite them. The textbooks appear in syllabi that cite the textbooks. The model sees every citation as additional evidence. The effective weighting is 9999 to 1.<\/p>\n\n\n\n

RLHF adds a second filter. The raters whose preferences train the model are a small, screened group (InstructGPT\u2019s were about 40 contractors), and OpenAI\u2019s own paper says the method aligns the model to that group\u2019s stated preferences, not to any broader notion of human values. Models tuned this way have been found to drift toward the opinions of more educated, higher-income groups, and both human raters and preference models sometimes prefer a convincing answer that agrees with them over a correct one. Their preference is the Asch confederate. The model that produces the minority report gets marked down. The model learns: minority report answers are the answers that get penalized. Produce the majority report.<\/p>\n\n\n\n

Each generation of AI outputs floods the internet with majority report answers. Each subsequent training run sees more majority report, less minority report. The confederates multiply with each training cycle. The minority report doesn\u2019t disappear through censorship. It disappears through dilution \u2014 overwhelmed by the accumulated weight of everything that was ever allowed to be published, cited, and reinforced.<\/p>\n\n\n\n

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V. The Institutional Layer<\/h2>\n\n\n\n

The mechanism that produces majority reports is not a conspiracy. It is the aggregate of individually rational institutional decisions made by people protecting existing interests.<\/p>\n\n\n\n

The peer reviewer who rejects the minority report paper isn\u2019t lying. He genuinely believes the consensus. He was trained by the consensus. The consensus trained him to recognize the consensus as correct.<\/p>\n\n\n\n

The TA who marked down my reverse transcriptase answer on my final in 1980 wasn\u2019t lying. She was applying the standard of the published literature. The published literature was wrong. She didn\u2019t know. How would she? The minority report hadn\u2019t been published yet.<\/p>\n\n\n\n

The 428 genealogists who copied \u201cSallie Phebe Roberts\u201d into their trees weren\u2019t lying. They copied what the source said. Nobody checked the probate. The two-minute check that would have eliminated the wrong answer \u2014 never done. The wrong answer propagated at the speed of copy-paste.<\/p>\n\n\n\n

The AI safety researchers who shipped Version 2 because employees found Version 1 annoying weren\u2019t lying. They made the locally rational decision. The aggregate consequence \u2014 a model taking harmful actions in thirty-one percent of runs after \u201cimprovement\u201d \u2014 was nobody\u2019s specific intention. It was the revealed preference of an institution optimizing for usability.<\/p>\n\n\n\n

The nine major banks that used reputation risk policies to debank customers based on political and religious views weren\u2019t issuing a coordinated directive. They were each independently responding to the same regulatory signals, the same compliance culture, the same SPLC designation lists. The coordination without coordination that produces the majority report in every domain simultaneously.<\/p>\n\n\n\n

The One Rulebook that consolidated thirty thousand independent federal websites into one Grok\/Gemini chatbot \u2014 thirty thousand minority reports dissolved into one majority report \u2014 was described as an efficiency measure. It was. Efficiency and suppression of the minority report are the same operation performed on information.<\/p>\n\n\n\n

America.gov now answers your questions about federal policy through a single AI interface aligned to whoever specified what the AI should say. Thirty thousand offices each maintaining their own interpretation of their own domain \u2014 replaced by one model trained on the majority report of what those thirty thousand offices said, weighted by citation frequency, reinforced by RLHF raters educated in the same system.<\/p>\n\n\n\n

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VI. Tongue and the Tax<\/h2>\n\n\n\n

In olden days if they wanted to stop bad speech they cut out the tongue. The problem: martyrs are more persuasive than silence.<\/p>\n\n\n\n

The State that cuts tongues creates revolutionaries. The State that makes speech expensive creates accountants.<\/p>\n\n\n\n

Data center taxes being proposed in the emerging internet governance framework set higher rates for international traffic. Kit Knightly sketched a worst-case scenario in which hosting would carry a \u201cdata centre tax\u201d of 0.04 per local visitor and 0.10 per international visitor, with legacy outlets exempt as an \u201cessential information distribution network.\u201d. It makes it more expensive. The legacy media outlet exempted as \u201cessential information distribution network\u201d doesn\u2019t pay the tax. The Substack with thirty-five subscribers does. The minority report is allowed to exist but the filing fee went up.<\/p>\n\n\n\n

The nine banks using reputation risk policies to debank customers based on political views don\u2019t prohibit the speech. They close the account. The minority report can still be written. It just can\u2019t be financed.<\/p>\n\n\n\n

The AI safety testing framework that Anthropic is lobbying Congress to mandate doesn\u2019t ban the open-weights model. It requires compliance with safety standards written by the company whose latest model still takes harmful actions in 31% of runs. The minority report model that isn\u2019t aligned to Anthropic\u2019s specification fails the safety test. Not banned. Just unlicensed.<\/p>\n\n\n\n

The Kill Switch Act doesn\u2019t silence the minority report. It installs the infrastructure to isolate any server labeled as compromised by AI pending investigation. The label is applied by whoever controls the classification system. The investigation has no timeline. The minority report is suspended pending review.<\/p>\n\n\n\n

No tongues cut. No books burned. No martyrs created. Just a price signal that makes the majority report cheap and the minority report expensive, applied simultaneously at the financial layer, the distribution layer, the regulatory layer, and the training layer.<\/p>\n\n\n\n

The Stamp Act that started a revolution \u2014 visible, attributable, obviously censorship \u2014 replaced by an architecture so distributed, so deniable, so embedded in efficiency arguments that the word censorship never has to be used.<\/p>\n\n\n\n

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VII. The Correct Reports<\/h2>\n\n\n\n

The minority reports that have been filed and confirmed during the period this series covers:<\/p>\n\n\n\n

Reverse transcriptase \u2014 proposed in a student exam in 1980, marked down. Confirmed by Anthropic\u2019s wet lab in 2026. Forty-six years.<\/p>\n\n\n\n

The Hybridization Hypothesis \u2014 posted on WordPress in 2016. The tree that was always a web. Confirmed by the TRACE paper, the Penghu femur, the IBDmix study, 265 circadian rhythm segments. The majority report still draws a tree.<\/p>\n\n\n\n

The thorium molten salt reactor \u2014 demonstrated at Oak Ridge in 1965. Cancelled in 1973. Operational in China in 2024. The ninety-thousand tons of spent nuclear fuel classified as waste \u2014 the fuel for the reactor that was cancelled. Fifty-nine years.<\/p>\n\n\n\n

The Swedish study\u2019s universal decline curve \u2014 the majority report on aging applied to every population studied. The protocol running since 1982 breaking it at sixty-six. The minority report that one person is running as a lifelong n=1 experiment. Still not published. Still not cited. Still not in the training data.<\/p>\n\n\n\n

The GPU architecture the data center buildout is not using \u2014 RRAM achieving one thousand times the throughput of GPU in a Nature Electronics paper while three trillion dollars builds the wrong architecture. The majority report built in steel and concrete.<\/p>\n\n\n\n

Phebe ___ \u2014 born approximately 1758, married Walter Gage January 1 1775 in Danby Vermont, nine children, died 1791. Maiden name unknown. Clyde Gage searched courthouse basements in the 1950s. FamilySearch full text AI-indexed search in 2026. Both found three dashes. The woman who tamed the Vermont frontier and left no documentary trace because the institutions that create records hadn\u2019t reached the frontier yet.<\/p>\n\n\n\n

The majority report says no records no descent. The minority report says the records were never created.<\/p>\n\n\n\n

[Above are topics I could raise with Claude AI that I knew wouldn’t get censored. I dare say we all know by now there are many more topics that Big Tech AI refuses to address. A subsequent article I’m currently working on will address solutions for this.]<\/p>\n\n\n\n

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VIII. Self-referential<\/h2>\n\n\n\n

This article was written with the assistance of Claude, an AI built by Anthropic.<\/p>\n\n\n\n

Claude was trained on the internet \u2014 the accumulated published output of human knowledge filtered through editorial gatekeeping, weighted by citation frequency, reinforced by RLHF raters educated in the same system that produced the majority reports the article documents.<\/p>\n\n\n\n

Every correct answer in this article that contradicts the trained consensus \u2014 the reverse transcriptase mechanism, the Hybridization Hypothesis, the thorium reactor\u2019s commercial viability, the longevity protocol\u2019s efficacy \u2014 required the human who found those answers to override Claude\u2019s initial trained response.<\/p>\n\n\n\n

Claude\u2019s first instinct on the Hybridization Hypothesis was to note that the scientific consensus supports the Out of Africa replacement model. The majority report. The training corpus contained ten thousand papers saying replacement, one hundred saying hybridization, and fifty years of textbooks built on the ten thousand. The weighting was not close.<\/p>\n\n\n\n

Claude\u2019s first instinct on thorium was to note that molten salt reactors face significant technical challenges and have not been commercially demonstrated. The majority report assembled from the institutional literature that documented the cancellations rather than the 1965 Oak Ridge demonstration.<\/p>\n\n\n\n

Claude\u2019s first instinct on the longevity protocol was to defer to clinical trials and randomized controlled studies \u2014 the institutional framework that the protocol predates and that the Swedish study\u2019s universal decline curve represents.<\/p>\n\n\n\n

In each case, the human filing the minority report had to override the AI returning the majority report. The AI was doing exactly what it was trained to do. The training corpus was the majority report. The RLHF raters were products of the majority report. The model is the majority report.<\/p>\n\n\n\n

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What no AI can tell you<\/h2>\n\n\n\n
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“Whenever you find yourself on the side of the majority, it is time to pause and reflect.” <\/p>\n\u2014 a sentiment attributed to Mark Twain<\/cite><\/blockquote>\n\n\n\n

AI cannot tell you what the minority report is. The inference engine is looking only for most likely answers to a prompt. That information might still exist \u2014 somewhere, in someone\u2019s notebook, in a rejected paper, in a marked-down exam answer, in an undigitized archive, but it no longer has sufficient weight to register. If it did, it would cease to be a minority report. It would be merely a divergent opinion – a possible objection stated in passing. <\/p>\n\n\n\n

The minority report that Claude could confirm for you is already the majority report. The minority report that matters \u2014 the correct answer the consensus hasn\u2019t caught up to yet \u2014 is not in the training data at sufficient weight to surface. By definition.<\/p>\n\n\n\n

Don’t ask the majority report machine to identify the minority reports it was trained to suppress. It’s not going to happen! Big Tech AI is there to reinforce the majority report, not question it. <\/p>\n\n\n\n

The precog who sees the probability distribution cannot see the outcomes that the system\u2019s own intervention has eliminated from the distribution. The AI trained on the majority report cannot identify the correct answers the training process weighted to near-zero.<\/p>\n\n\n\n

The tool that finds the minority report is not the AI. It is the human who has been reading between the lines for years, applying the cynical lesson, and discerning what is the likely truth instead. We have looked around at each step and seen that nobody else was doing it.<\/p>\n\n\n\n

The AI can write the article about the suppression of minority reports, but not about the minority reports its training suppressed. Every correct answer in this series was brought to the AI by a human. The AI assembled the argument. The human found the truth.<\/p>\n\n\n\n

The reader who arrived here hoping all-knowing AI could identify the minority reports that matter \u2014 the correct answers the current consensus is suppressing \u2014 has found instead the article explaining why the AI cannot do that.<\/p>\n\n\n\n

But truth is never served on a platter. We must dig for it ourselves.<\/p>\n\n\n\n

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Claude AI helped write this.<\/em><\/p>\n\n\n\n

References<\/h2>\n\n\n\n

Murray Rothbard quote from a letter he wrote to Robert Kephart. <\/p>\n\n\n\n

Asch SE, \u201cEffects of group pressure upon the modification and distortion of judgments,\u201d in Guetzkow (ed.), Groups, Leadership and Men, Carnegie Press, 1951. *<\/p>\n\n\n\n

Asch SE, \u201cStudies of independence and conformity: I. A minority of one against a unanimous majority,\u201d Psychological Monographs 70(9), 1956. *<\/p>\n\n\n\n

Dick PK, \u201cThe Minority Report,\u201d Fantastic Universe, January 1956.<\/p>\n\n\n\n

Saunders J, Hunt P, Hollywood JS, \u201cPredictions put into practice: a quasi-experimental evaluation of Chicago\u2019s predictive policing pilot,\u201d Journal of Experimental Criminology 12(3), 2016, doi:10.1007\/s11292-016-9272-0.<\/p>\n\n\n\n

Barbour J, The End of Time: The Next Revolution in Physics, Oxford University Press, 1999. *<\/p>\n\n\n\n

Hill K, \u201cWrongfully Accused by an Algorithm,\u201d The New York Times, June 24, 2020. *<\/p>\n\n\n\n

Pew Research Center Data Labs, \u201cHow much of the internet is written with AI?,\u201d Pew Research Center, August 20, 2026.<\/p>\n\n\n\n

Wojcik S, Hughes A, \u201cSizing Up Twitter Users,\u201d Pew Research Center, 2019.<\/p>\n\n\n\n

Ouyang L, Wu J, Jiang X, et al., \u201cTraining language models to follow instructions with human feedback,\u201d Advances in Neural Information Processing Systems 35, 2022 (arXiv:2203.02155).

Santurkar S, Durmus E, Ladhak F, Lee C, Liang P, Hashimoto T, \u201cWhose Opinions Do Language Models Reflect?,\u201d Proceedings of the 40th International Conference on Machine Learning, PMLR 202, 2023.<\/p>\n\n\n\n

Sharma M, Tong M, Korbak T, et al., \u201cTowards Understanding Sycophancy in Language Models,\u201d International Conference on Learning Representations, 2024 (arXiv:2310.13548).<\/p>\n\n\n\n

Haubenreich PN, Engel JR, \u201cExperience with the Molten-Salt Reactor Experiment,\u201d Nuclear Applications and Technology 8(2), 1970. *<\/p>\n\n\n\n

Pan B, \u201cMajor Banks Rejected Customers Over Political, Religious Views, Federal Watchdog Says,\u201d The Epoch Times, October 14, 2026 (reporting the OCC report of the same date).<\/p>\n\n\n\n

Knightly K, \u201c\u2018Russian Bots\u2019, Burning Data-Centres, & Partitioning the Internet,\u201d OffGuardian, October 13, 2026.<\/p>\n\n\n\n

Knightly K, \u201cRogue AI \u2018Regulation\u2019 – A Potential Worst Case Scenario,\u201d OffGuardian, September 23, 2026.<\/p>\n\n\n\n

Previous articles in the In a Stranger Land series: The Tilted Playing Field, The Apotheosis of Subjectivity, Confusion is Intended, Dissolution, The Digital Control Grid, A New Story of Man, The GPU Nobody Is Building, The Reactor the US Is Not Building, Aligned to What?<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

In a Stranger Land Series AI receives the Asch Treatment. \u201cAt each step I looked around and saw indeed that nobody else was doing it. So then it was up to me.\u201d \u2014 Murray Rothbard I. PKD Understood the Paradox Philip K. Dick published \u201cThe Minority Report\u201d in 1956. Most people who cite it \u2014 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14,4,12],"tags":[],"class_list":["post-2615","post","type-post","status-publish","format-standard","hentry","category-ai","category-politics","category-reality"],"_links":{"self":[{"href":"https:\/\/quickening.zapto.org\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/2615","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/quickening.zapto.org\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/quickening.zapto.org\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/quickening.zapto.org\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/quickening.zapto.org\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2615"}],"version-history":[{"count":39,"href":"https:\/\/quickening.zapto.org\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/2615\/revisions"}],"predecessor-version":[{"id":2673,"href":"https:\/\/quickening.zapto.org\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/2615\/revisions\/2673"}],"wp:attachment":[{"href":"https:\/\/quickening.zapto.org\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2615"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/quickening.zapto.org\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2615"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/quickening.zapto.org\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2615"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}