Inside out and upside down? Perceptions of temporary employment histories in the time of COVID
Bibliographic record
Abstract
Purpose Perceptions of employment histories are important insofar as they influence future job prospects. Critically, in light of the current pandemic, wherein many individuals are likely to have unanticipated employment gaps and/or temporary work experiences, this exploratory study aims to seek a better understanding of the signal associated with temporary employment histories, which is particularly germane to individuals' employment trajectories and a successful labour market recovery. Design/methodology/approach Drawing primarily on signalling theory and using a simulated hiring decision experiment, the authors examined the perceptions of temporary employment histories, as well as the period effect of COVID-19, a major exogenous event, on the attitudes of fictitious jobseekers with standard, temporary and unemployment histories. Findings The authors find that prior to COVID-19 unemployed and temporary-work candidates were perceived less favourably as compared to applicants employed in a permanent job. During the COVID-19 pandemic, assessments of jobseekers with temporary employment histories were less critical and the previously negative signal associated with job-hopping reversed. This study’s third wave of data, which were collected post-COVID, showed that such perceptions largely dissipated, with the exception for those with a history of temporary work with different employers. Practical implications The paper serves as a reminder to check, insofar as possible, preconceived biases of temporary employment histories to avoid potential attribution errors and miss otherwise capable candidates. Originality/value This paper makes a unique and timely contribution by focussing and examining the differential effect of economic climate, pivoted by the COVID-19 pandemic, on perceptions of temporary employment histories.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".