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Record W7048899594

Master’s Program in Money and Finance (MMF)

2010· book· en· W7048899594 on OpenAlexfundno aff

Bibliographic record

VenuePublication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) · 2010
Typebook
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersInstitut für WeltwirtschaftBanco de EspañaUniversity of CyprusNational and Kapodistrian University of AthensJohannes Gutenberg-Universität MainzEuropean University InstituteUniversitat Pompeu FabraYork UniversityUniversity of PennsylvaniaHong Kong Institute for Monetary ResearchYale UniversityComer Science and Education FoundationBrandeis UniversityHarvard University
KeywordsGermanMonetary policyLife insuranceGateway (web page)Money marketJoint venture
DOInot available

Abstract

fetched live from OpenAlex

The Master’s program in Money and Finance (MMF) is an innovative joint venture of the Department of Money and Macroeconomics and of the Department of Finance, both located in the new House of Finance. The program offers promising students from all over the world an intellectually stimulating and challenging setting in which to prepare for their professional careers in central banking, commercial banking, insurance and other financial services. By being located in Frankfurt, one of the world's leading financial centers and the only city in the world with two central banks (the ECB and the German Bundesbank), it offers unique opportunities for interaction with practitioners. The program is taught exclusively in English; knowledge of German is not required for admission to, or completion of the program. It has been designed with a view to establishing itself as a leading Masters program integrating studies in monetary economics, macroeconomics and finance and a major gateway to high-profile jobs in the banking and financial sector.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.076
Threshold uncertainty score0.254

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0760.045

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.210
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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