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Record W6957895782 · doi:10.6068/dp15cf4a4855786

RANKING: Easy Analytic Software, Inc., Easy Analytic Software Inc. (EASI). Consumer Expenditures - 2016: Expenditures – Home | State: New York | Socioeconomic Indicator: Window air conditioners, Renter, 2016. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 050-428-002

2017· other· en· W6957895782 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCensusSoftwareSocioeconomic statusConsumer Expenditure SurveyMarket researchRange (aeronautics)Quarter (Canadian coin)Consumer expenditureSurvey data collection

Abstract

fetched live from OpenAlex

Easy Analytic Software, Inc., Easy Analytic Software Inc. (EASI). Consumer Expenditures - 2016: Expenditures – Home | State: New York | Socioeconomic Indicator: Window air conditioners, Renter, 2016. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 050-428-002 Dataset: Average annual household expenditures for housing and various household products other than food. Shows average annual household expenditures among consumers in the United States for 600+ categories of expenditures, representing a model of spending potential. Data are from the nationwide Consumer Expenditure Survey (CEX), conducted annually by the US Bureau of Labor Statistics and the US Census Bureau. Easy Analytic Software Inc. (EASI) applies its statistical modeling technology and demographic profiles of various geographic areas to CEX data. The data are modeled against updated demographic estimates using a proprietary method to develop estimates for 2016 and 5-year projections. Definitions of individual expenditure items can be found in the CEX glossary: http://www.bls.gov/cex/csxgloss.htm http://www.easidemographics.com/ Category: Prices, Consumption, and Cost of Living Subject: Households, Expenditures, Consumer Products, Consumer Spending Source: Easy Analytic Software Inc. (EASI) Easy Analytic Software Inc. (EASI) is a New York-based software engineering and statistical modeling firm that specializes in consumer demographics. Using input data from the Census Bureau, Bureau of Labor Statistics, and Mediamark, EASI develops model-based indicators of the demographic characteristics, consumer spending, and behavior patterns for a wide range of geographic areas—state, counties, census tracts, and block groups. Mediamark is a market research firm that annually publishes The Survey of the American Consumer, based on detailed interviews of 26,000 households. http://www.easidemographics.com/

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)0.004
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.685
Threshold uncertainty score0.978

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.032
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.011
Science and technology studies0.0010.000
Scholarly communication0.0040.004
Open science0.0030.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.3150.282

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 head0.027
GPT teacher head0.285
Teacher spread0.258 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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
Published2017
Admission routes1
Has abstractyes

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