MétaCan
Menu
Back to cohort
Record W1550906702

The Economic Development Of Bukovina (I) The First Period Of Austrian Rule: 1774 - 1849

2010· article· en· W1550906702 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueRePEc: Research Papers in Economics · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsAnnexationEmpirePeriod (music)State (computer science)Quarter (Canadian coin)EconomicsBusinessEconomic policyEconomyEconomic growthPolitical scienceGeographyArchaeologyLaw
DOInot available

Abstract

fetched live from OpenAlex

The annexation of Bukovina by the Habsburg Empire was a decisive moment for its economic and social evolution. The measures taken by the new administration, right after 1775, created the basis for a rapid economic development, which had positive effects on the standard of living and the quality of life in the region. The administrative reform, the transformation of the legal system, the investments in infrastructure, the new fiscal rules, as well as all the other economic policies which were promoted, generated a considerable economic progress. However, we shouldn’t omit the contribution of the human factor, the period analyzed in this paper being characterized by deep demographic transformations. Economically, after the occupation of the province, Bukovina underwent spectacular transformation, stimulated by the development of infrastructure and by the influx of specialists from other parts of the empire, as well as by the new opportunities offered by the market and the State’s policies promoting certain fields of activity. Industry recorded the most spectacular growth, the most dynamic industries being breweries, distilleries, milling, logging, the production of glass and paper etc. At the same time, the discovery of some pitch pits, salt water springs and peat deposits spurred the development of other industries.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.276
Teacher spread0.246 · 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