Burning Glass Technologies’ data use in policy-relevant analysis
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.
Bibliographic record
Abstract
This work proposes an analysis of the statistical properties and distributional characteristics of Burning Glass Technologies' (BGT) data on online job openings from platforms and companies, at the occupation level. BGT data are compared to official data on employment by occupation to assess their occupation-specific representativeness. This work further proposes weighting schemes aimed at making BGT-based analysis fully representative at the occupation and country levels, where appropriate. The analysis encompasses six economies – Australia, Canada, New Zealand, Singapore, the United Kingdom and the United States – for the period 2010-19. Overall, it finds that BGT data exhibit good statistical properties and are a useful source of timely information about labour market demand, especially for high-skill occupations and recruitment processes that are more likely to happen online.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it