MétaCan
Menu
Back to cohort
Record W6922249915 · doi:10.11587/qjsqye

Mikrocensus 1993, 2. quarter: Equipment of Households

2020· dataset· en· W6922249915 on OpenAlexaboutno aff

Bibliographic record

VenueAUSSDA - The Austrian Social Science Data Archive · 2020
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsBackupSpare partPopulationField surveyStandard of livingSurvey data collectionSurvey methodologyQuarter (Canadian coin)

Abstract

fetched live from OpenAlex

This Mikrozensus special survey contains questions on the equipment of households with electric appliances, and the disposal of old electric appliances, environmental conditions of housing and the need of repair of the apartment, the existence of holiday apartments, new purchases (within the last two years), social-statistical data, the existence of vehicles (new purchases and the disposal of the old vehicle) and season tickets for public transport, indicators for holiday- and spare time habits, financial backup and the net income of all persons (except those who are self-employed or help in the family business). The survey mainly follows the Mikrozensus survey from June 1979 (Mikrozensus MZ7902), June 1984 ( Mikrozensus MZ8402 ) and June 1989 (Mikrozensus MZ8902).The survey offers the possibility to gather information on the material living conditions of the Austrian population (and their change over time). Especially important is the connection of the indicators for standard of living and the household income. New is the increasingly important field of environmental protection (disposal of old electric appliances).

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)0.005
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.080
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.010
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0480.057

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 head0.100
GPT teacher head0.341
Teacher spread0.241 · 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
GenreDataset

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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAUSSDA - The Austrian Social Science Data ArchiveFrench-language works237,207