Thinking about careers: reflexivity as bounded by previous, ongoing, and imagined experience
Bibliographic record
Abstract
Many social scientific studies have shown the positive effects of self-awareness and reflexivity in shaping individuals’ career paths. However, using life- and work-history interviews conducted with salespersons in Toronto, Canada, I find that high levels of self-awareness – as demonstrated by active deliberation over one’s career – has both positive and negative results in terms of career outcomes. Respondents whose careers initially progressed as they expected tended to benefit from reflexively managing their careers. However, the benefits of reflexivity were mixed for respondents whose careers did not begin as planned. This group of respondents sometimes benefited from thinking about alternatives; but also, sometimes, cast themselves outside of careers that they deemed to be ‘normal’. Observing the way that some respondents formed new career plans based on unmet expectations, and began to pursue those emergent plans, I argue that scholarly uses of reflexivity should incorporate its function as a potential career trajectory anchor.
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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.054 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".