Bibliographic record
Abstract
While previous studies have focused on the supply side of employment transitions for older workers, there is a lack of understanding of the demand side of the labor market. Given that almost half of older workers experience multiple employment transitions prior to retirement, this study aims to investigate job availability in a local market for older workers and identify the required skills for available jobs. This study used online job posting data in a southern community in the U.S. which was collected from three job posting sources during the fourth quarter of 2021. Qualitative data, with a total of 5,160 job postings, were merged with the O*NET database. Descriptive analyses were performed to account for job availabilities and required skills. Additionally, 2021 American Community Survey data derived from IPUMS USA were used to investigate the local community’s older workforce. Job opportunities emerged in a wide range of occupations, with higher concentrations in three occupational groups. Occupations with high proportions of older workers aged 65 or older required relatively fewer work requirements such as education, work experience, and skills. The study also identified generally applicable job requirements, except for technology skills, across occupational groups divided into two tracks by the level of job requirements. This study identified a poor fit between the local job market and older workers, which highlights the need for pre- and post-response to the aftermath of economic downturns and continuous technology skill development.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".