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IMAGES-005-HOUSE_OVERSIGHT_020367.txt,"215
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The final choice he was made to board a non-stop flight to Moscow on June 23, 2013.
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To remain in Hong Kong once a criminal complaint was leveled against him would have meant
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that, at the very minimum, Hong Kong authorities would seize him and the alleged stolen property
|
of the US government in his possession. Even if he was released on bail, the Hong Kong
|
authorities would almost certainly retain all the NSA and GCHQ files he had gone to such lengths
|
to steal. He also would not be allowed to leave Hong Kong and possibly denied any access to the
|
Internet. As he demonstrated by his subsequent actions, this option was not acceptable to him.
|
Once the U.S. criminal complaint was unsealed on June 21, 2013, which became all but
|
inevitable after his video, his only route out of Hong Kong went through two adversaries of the
|
United States, China and Russia. China, as far as is known, did not offer him sanctuary.
|
According to one U.S diplomat, it may have already obtained copies of Snowden’s NSA files, and
|
did not want the problem of having Snowden defect to Beijing. In any case, if it had not already
|
acquired the files. It could assume it would receive that intelligence data from its Russian ally in
|
the intelligence war. Whatever its reason, China did not use its considerable power in Hong
|
Kong to block Snowden’s exit.
|
Nor did Snowden obtain a visa to any country in Latin America or elsewhere during his month-
|
long stay in Hong Kong. As in the oft-cited Sherlock Holmes’ clue of the dog that did not bark,
|
Snowden’s lack of any visas in his passport strongly suggests that he had not made plans to go
|
anyplace but where he went: Moscow. His actions here, including his contacts with Russian
|
officials in Hong Kong, speak louder than his words.
|
Snowden chose, if he had any choice left at all, the Russian option. Just as he believed Chinese
|
intelligence could protect him in Hong Kong from the United States, he could assume that the
|
FSB could protect him in Moscow from the United States. He was not entirely na* ve about its
|
capabilities. During his service in the CIA, he had taken a month-long training course at the CIA’s
|
“farm” at Fort Peary in which counterintelligence officer taught about the capabilities the Russian
|
security services
|
To be sure, he might not have known that Moscow would be his final destination. He may
|
have naively believed that Russia would allow a defector from the NSA who claimed to have had
|
access to the NSA’s sources in Russia and China leave Moscow before its security services
|
obtained that information. But that was not to be.
|
It is not uncommon for a defector to change sides in order to find a better life for himself in
|
another country. Some defectors flee to escape a repressive government or to find one in which
|
they believe they are more closely attuned. But Russia is ordinarily not the country of choice for
|
someone such as Snowden seeking greater civil liberties and personal freedom. So why did
|
Snowden choose Russia for his new life?
|
The four choices that Snowden made in 2013 did not come out of the blue. They all were
|
planned out well in advance. He applied for the job to Booz Allen in February 2013, more than a
|
month before leaving his job at Dell. He applied to Booz Allen for his medical leave, although in
|
fact he had no medical problem, a month before departing for Hong Kong. He brought with him
|
to Hong Kong enough cash to pay his living expenses, according to him, for the next two years.
|
He arranged the encrypted channel with Poitras in February 2013, three months before he would
|
induce her to come to Hong Kong. He made contact with a foreign diplomatic mission at least a
|
month before flying to Moscow and, at some point, met with Russian officials, who arranged a
|
visa-less entry for him. He called Assange in London to arrange for Wikileaks help, 13 days
|
HOUSE_OVERSIGHT_020367
|
"
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IMAGES-001-HOUSE_OVERSIGHT_011104.txt,"What matters is return. I don’t have to specify “risk-adjusted” return so long as |
|
describe the collective scale alone. Collective return is implicitly average-risk return.
|
I prioritize it on the reasoning that optimizing employment of people and plant is
|
implicit, and that optimizing means putting them to work most productively rather
|
than over the most hours.
|
If policy maximizes rate of return, at the collective scale, it will maximize true output
|
perforce. Return is output divided by total capital producing it. More return is more
|
output per unit capital. Putting idle plant and people to work, in a slump, is a step in
|
the right direction. But it doesn’t get the job done unless they work productively.
|
Even putting money under the mattress is better than investing at a loss. Zero
|
return is better than negative return. | accept Keynes’ distinction between new
|
investment and transfer payments. But I see the latter as part of the mechanics that
|
ends up in the former. Maximize return, and full employment will happen.
|
Keynes’ opposition is now mostly the Chicago school and other “freshwater” schools
|
bordering the Great Lakes and along inland rivers. Somehow the taste for Keynesian
|
intervention resonated best in “saltwater” seaboard school such as Harvard, MIT,
|
Stanford, and University of California. It is probably no coincidence that the
|
saltwater states are the “blue” ones tending to vote Democrat, while the freshwater
|
ones are the “red” ones favoring Republicans. (I call myself a free market Democrat,
|
whether or not that’s a contradiction in terms.) Freshwater views tend to oppose
|
intervention, but accept Keynesian basic definitions and equations such as the
|
Y =] +C doctrine and the distinction between “attempted saving” and investment.
|
It is these I question.
|
I] don’t think much of his view that intended saving (consumption foregone)
|
becomes actual saving only if invested, and becomes an equal amount of physical
|
capital growth if it is. Then (actual) net saving, net investment and physical capital
|
growth would become synonymous. | said why I prefer a language where saving
|
and investment are synonymous in the first place. What matters is rate of return.
|
Chapter 8 Banks, Money and Macroeconomics 2/8/16 15
|
HOUSE_OVERSIGHT_011104
|
"
|
TEXT-001-HOUSE_OVERSIGHT_031683.txt,"From: J [[email protected]]
|
Sent: 5/30/2019 9:34:38 PM
|
To: Michael Wolff
|
Disclaimer
This dataset is a reupload of a previously circulating public dataset. The contents may include unverified, incomplete, disputed, or inaccurate information and should not be interpreted as factual, authoritative, or as proof of guilt for any individual. This dataset is provided strictly for research, archival, and educational purposes, such as analysis of information propagation, data preservation, or media studies. No claims are made regarding the accuracy, authenticity, or legitimacy of the materials contained herein. If the dataset is demonstrated to be falsified, inaccurate, or if the original creator or relevant rights holders request removal, it will be taken down promptly. This dataset should not be relied upon as a source of truth. The maintainer of this repository does not endorse, affirm, or validate any claims contained within the dataset. This reupload is intended solely to preserve access to publicly released materials in their extracted text form. The rest of this readme is a copy of the original repository.
U.S. House Oversight Epstein Estate Documents
The motivation for curating this dataset is to enable transparent exploration of the Epstein estate documents. The goal is to empower AI practitioners, researchers, and enthusiasts to build RAG based systems that can identify patterns, connections, and insights that are difficult to obtain through manual inspection.
Usage and Responsibilities (Required Reading)
This dataset is provided for research and exploratory analysis with a focus on:
- Evaluating information retrieval and retrieval augmented generation (RAG) systems.
- Developing and testing search, clustering, and summarization methods on a real world corpus.
Users are responsible for:
- Using the dataset only for lawful purposes and in accordance with institutional and ethical review requirements.
- Treating individuals mentioned in the documents with respect, and avoiding sensationalism or misuse of sensitive material.
- Clearly distinguishing model generated content and exploratory findings from verified facts, and citing primary sources where appropriate.
It is not intended for:
- Fine-tuning language models.
- Harassment, doxing, or targeted attacks on any individual or group.
- Attempts to deanonymize redacted information or circumvent existing redactions.
- Making or amplifying unverified allegations as factual claims.
All use must comply with applicable law, institutional policies, and the terms of the original House releases. See the “Legal and copyright status” and “Ethical and content warning” sections below before working with this corpus.
Source (All data derived from publicly released materials)
All documents originate from the public release “Oversight Committee Releases Additional Epstein Estate Documents” on the official House Oversight Committee website (press release dated November 12, 2025):
The underlying materials are distributed via a Google Drive structure maintained by the Committee. This dataset is an independent derivative collection built from that release and is not an official product of the U.S. House of Representatives or the Committee on Oversight and Government Reform.
Dataset contents
- Documents: Over 25,000 plain text files derived from the committee’s public releases organized in a single csv file
- Source Folders:
TEXT/– Files that were originally text-based (e.g., PDFs, emails) and converted to plain text.IMAGES/– Image files (primarily JPG) converted to text via OCR.
Filenames preserve the relative path and naming conventions from the original Google Drive release, to facilitate cross-referencing back to the official source files.
Processing
- All image files under the
IMAGES/directory (approximately 20,000 JPGs) were converted to machine-readable text using the open-source Tesseract OCR engine. - Native text-based files under
TEXT/were converted to plain text using standard tools (e.g., PDF/text extraction) without manual editing. - No manual content editing, summarization, or redaction has been performed beyond:
- basic file organization,
- text extraction / OCR,
- and any redactions already present in the official House releases.
As a result, the corpus may contain:
- OCR noise and misrecognized characters
- Broken formatting
- Redaction blocks, stamps, or markers inherited from the original scans
Legal and copyright status (non-authoritative)
- The original underlying documents were created by various private individuals and entities, not by the dataset maintainer.
- The documents are sourced from releases published by the U.S. House Committee on Oversight and Government Reform. The release webpages themselves carry standard copyright notices (© 2025 Committee on Oversight and Government Reform), and many individual documents are likely protected by copyright held by their original authors or rights holders.
- This dataset:
- Does not assert any ownership over the underlying documents.
- Does not grant any license to reproduce, distribute, or create derivative works from the underlying texts beyond what may already be permitted by law (e.g., fair use or similar doctrines in your jurisdiction).
- Users are solely responsible for ensuring that their use of this corpus complies with applicable copyright law, privacy law, institutional policies, and the terms of the original House releases.
Nothing in this dataset card constitutes legal advice. If you plan to use this corpus in a public-facing product, for model training, or at scale, you should seek independent legal counsel.
Ethical and content warning
The documents contain material related to:
- Sexual abuse and exploitation
- Trafficking
- Violence and other highly sensitive topics
- Unverified allegations, opinions, or speculation
Intended use and limitations
Recommended / common use cases include:
- Text mining and exploratory analysis of the public record surrounding the Epstein estate documents.
- Search / retrieval experiments (e.g., indexing, ranking, IR/RAG prototypes) conducted in controlled or research settings.
- Qualitative review by journalists, historians, or legal scholars.
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