The lived experience of underemployed older workers in Canada
Bibliographic record
Abstract
Population ageing is shaping Canadian demographics, transforming the labour market, and de-peripheralizing older workers. Being healthier than their preceding cohorts, older workers are staying longer in work and have been driving the growth of employment. Despite they are a skilled societal resource, many of them find themselves in an underemployed situation, which has become a growing concern since the global financial meltdown in late 2008. This research utilizes a mixed-method design combining quantitative analyses using secondary data with qualitative in-depth semi-structured interviews. A mixed-method design was deemed appropriate for this inquiry due to limited prior evidence and non-availability of other relevant data sources. Underemployment could result in a diverse range of negative consequences. This thesis investigates the characteristics of underemployment and its impact on older workers in Canada, with a particular focus on subjective well-being. It also looks at the meaning of underemployment ascribed to and the lived experience of a sample of 15 interview participants. The findings from the quantitative analysis informed the design of the qualitative study, which provided the nuances to help understand the former. There are policy implications accrued from both quantitative and qualitative findings. While financial deprivation was the most common experience shared by the participants, the consequences of underemployment are not uniform. They are moderated by various characteristics of an individual, variations in work-role centrality and subjective perception formulated uniquely over the life-course. The findings also illustrate the varied and complex interactions between agency and structure that shape the lived experience of underemployed older workers. This thesis seeks to address the paucity of research concerning older worker underemployment in Canada, in relation to the knowledge about their subjective perceptions. It considers multi-dimensions of underemployment in its analyses, supports calls for standardization of definitions and raising awareness of underemployment as a social phenomenon.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".