Underemployment of highly qualified labour in advanced capitalism: trends and prospects
Bibliographic record
Abstract
‘Employers know that they can hire worldwide now … so, there is limitless supply of people … who can do the job … . they’re all qualified, most of them are actually over-qualified … . I’m a wage slave basically, I don’t think we have very much social status … . we are replaceable workers … I mean, the employer holds all the cards really. We are salaried employees … no different from any other worker.’ (Owen, automation engineer 2017)(Respondent to CWKE interview with engineers)Post-secondary graduates and professional employees in particular are widely regarded as highly qualified strategic resources in advanced capitalist ‘knowledge economies.’ However, there is mounting evidence that these ‘knowledge workers’ are experiencing increasing underemployment as well as diminishing involvement in continuing learning and some decline in job satisfaction. Trends in these factors are documented primarily on the bases of a series of national surveys of the labour force in Canada between 1982 and 2016. Prospects for more critical attitudes to working conditions as well as shifts in theorising and policy-making regarding relations between employment reforms and educational reforms are considered.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".