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Record W6990073358

Conducting surveys of EMSD clients to provide market information to their clients

2019· article· en· W6990073358 on OpenAlexaboutno aff

Bibliographic record

VenueUpjohn Research (W.E. Upjohn Institute for Employment Research) · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic, Social, and Health Studies
Canadian institutionsnot available
Fundersnot available
KeywordsData collectionQuarter (Canadian coin)Metropolitan areaGeneral partnershipWork (physics)WageCover (algebra)Compensation (psychology)
DOInot available

Abstract

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The State of Michigan's Labor Market Information (LMI) is able to provide information about wages for occupations down to the Metropolitan Statistical Areas (MSA) in the state. But the data are limited to a select group of occupations, the most recent data available are for 2017, and the data do not cover non-MSA locations very well. The LMI data does provide, for those occupations and geographies included, a wide range of wages including mean and median wages as well as "entry" wage.\nThis research will take the data collection to a more detailed level than provided by LMI. Through a partnership with Research and EMSD, data will be collected in a more timely manner, and the data will be collected on a broader set of topics beyond wages. In this process, EMSD staff will work with employers in the region to collect data via online survey methods each quarter. The topic each quarter will be different, but for Q1 2019 the topic will be starting wages by occupations and industries. In Q2 2019, the topic will be on benefits that will help to round out the compensation question. Topics for Q3 and Q4 for 2019 will be determined based on client needs for information and determined collaboratively with EMSD and Research.\nUpjohn will partner on the data collection side with Kent State University at Start (KSU/Stark). KSU/Stark has more than 12 years of experience in collecting these types of data and have a well-tested online tool to collect both starting wage and benefit data. In collaboration with the Upjohn team, they will develop surveys for Q3 and Q4. KSU/Stark will, at the end of the survey period, provide the data to Upjohn for analysis. KSU/Stark will not use any data of the Upjohn data for research or to provide comparisons to other similar client data.\nUpjohn staff will provide an analysis and summary of the data that will be useful to companies in better understanding current regional market conditions. The summaries and reporting will be developed collaboratively between EMSD and Research. At no time will a single firm's data be reported, and checks will be in place to be sure that confidentiality of the firm will be maintained.

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 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)0.027
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.004

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 head0.345
GPT teacher head0.417
Teacher spread0.072 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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
Published2019
Admission routes1
Has abstractyes

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