Conducting surveys of EMSD clients to provide market information to their clients
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.061 | 0.026 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".