ON TIME AND OFF TIME CAREER TRAJECTORIES IN THE NEW ECONOMY: THE CASE OF INFORMATION TECHNOLOGY WORK
Bibliographic record
Abstract
There are significant changes happening in the world of work, under the guise of a 'new' economy that embraces innovation and technology, flexible labour, and risk and uncertainty. With the proliferation of less conventional work arrangements and new career forms, traditional notions of employment careers and life trajectories are in flux. To better understand these trends and potential consequences for workers, this research considers the relative timing of entry to a prototypical new economy sector, information technology (IT). Using the life course perspective as a guide, I investigate entry pathways to IT, assessing who is 'on time' (i.e., made a fairly direct transition from school to IT work in young adulthood with one's age cohort) and who is 'off time' (i.e., entered IT at a later life stage, often returning to school after some time away as part of the process). I then explore some of the nuances of on and off time paths, comparing motives, experiences of (re)training and IT work, career expectations and perceptions of future career trajectories. To do this, I employ a subsample of 135 Canadian IT workers and interview and survey data from a larger study, Workforce Aging in the New Economy. In this sample, a significant proportion of respondents (40 percent) are off time, and certain segments (women, older workers) are disproportionately so*. On time pathways tend to be better supported by social norms and institutions such as schools. Moreover, they appear to be associated with higher levels of education and holding the most highly skilled IT occupations, and presumably, more of the related benefits. Findings suggest that many off time entrants deal with unique struggles in education and labour market entry, including retraining challenges, greater difficulty securing appropriate employment in the field and lowered expectations in terms of their career development and satisfaction with working life. In a labour market environment that expects and encourages multiple changes in jobs or careers across the life course, this research reveals the need for structural change in work and educational environments to make such transitions easier and more efficient, especially later in life.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".