Doing meta-work to navigate conventionally masculinist careers and work norms in professional workplaces
Bibliographic record
Abstract
Research has shown that while conventionally masculinist career models no longer necessarily correspond to how individuals live their lives, they continue to be prevalent and idealized in most corporate, professional workplaces. These models leave few alternatives for men, as well as women who want or need to organize their work differently. This chapter explores male privilege in entering and unfolding careers from the perspective of meta-work – hidden, invisible and laborious work – performed by professionals as they seek to live up to the traditional and normative ideals surrounding socially prestigious careers and identities. Meta-work is also an important coping mechanism through which professionals deal with feelings of inadequacy, dilemmas and disappointments surrounding their conventionally masculinist careers. Two empirical case studies provide insights into the workings of meta-work from the perspective of white, professional men in various national contexts: lawyers in Finland and Canada who pursue careers in competitive corporate environments, and Finnish and US professionals who have opted out of high-powered careers to live and work on their own terms. The chapter shows that living up to masculinist careers and work norms requires continuous meta-work to advance one’s own position within the internal hierarchical ladder of professional workplaces.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".