Gendered career‐making practices: On doing ambition or how managers discursively position themselves in a multinational corporation
Bibliographic record
Abstract
Although in the last decades there have been an increasing number of female managers, few make it to top management positions. In this study we want to gain insight into the persistence of the ‘glass ceiling’ by analyzing, from an ethnomethodologically informed discursive approach, how managers discursively position themselves in career making practices. Our study is located at the Dutch site of a multinational corporation where no women were found in higher positions, despite their growing presence in management positions. We aim at unraveling the implied membership competencies to participate in career making practices. In line with research on gendered organizations we consider these competencies to be gendered. In a detailed discursive analysis of interview material we identified an underlying paradox of ‘doing ambition’. We conclude that the women in this study who are ‘doing ambition’ are inevitably caught in a double bind position. The (re)production of gender inequality can be understood in terms of this double bind that is normalized in the organization. We argue that the discursive approach demonstrated in this article, is suitable for gaining insight into the often paradoxical demands managers, and especially women, face in daily career practices.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".