Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze employer responses to vacancies and skill shortages by adopting certain workplace practices. Design/methodology/approach Making use of the longitudinal nature of the Workplace and Employee Survey, a nationally representative sample of Canadian organizations, the paper applies both linear and probit models to examine incidence of positive vacancies and vacancy rates and subsequent adoptions of various workplace practices in response to such vacancies and skill shortages. Findings Employers respond to labour and skill shortages in a number of ways, focusing more on short‐term and less costly solutions, such as adoption of flexible working hours and increases in overtime hours, greater reliance on flexible job design and part‐time workers, and implementation of self‐directed work groups and problem‐solving teams. There is no evidence that workplaces would raise employee wages or fringe benefits to alleviate shortages. Practical implications In the absence of a well‐developed internal market, firms are likely to continue using short‐term and less costly solutions. Governments should work with firms, workers and their representatives and act strategically to resolve issues of timely identification of skill shortages in order to make informed decisions and put mechanisms in place to address such shortages. Originality/value The results are based on a national longitudinal survey and a number of important practical and policy implications are discussed in the paper
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".