Does gendered wording in job advertisements deter women from joining start‐ups? A replication and extension of Gaucher, Friesen, and Kay (2011)
Bibliographic record
Abstract
Abstract Research Summary Gaucher, Friesen, and Kay (2011: “GFK” hereafter) found that women perceive jobs to be less appealing when job adverts use masculine wording—a result they attributed to women's lower evaluations of “belongingness.” As masculine wording is used more often in male‐dominated jobs, GFK concluded that gendered wording in job adverts may deter women from entering such jobs. In light of growing general interest in joining new ventures (“start‐ups”), we replicate and extend GFK's study to compare start‐ups and established firms. Interestingly, we find that GFK's original findings are replicated in the context of start‐ups, but not in established firms. We propose and adduce evidence that the unique context of start‐ups may prime women to respond especially sensitively to gendered wording, via positive expectancy violation. Managerial Summary This article builds on a previous study that found masculine wording in job adverts deters women from entering male‐dominated jobs. Our purpose is to try to replicate these findings using a more recent sample of data and distinguishing new ventures (“start‐ups”) from established firms. Interestingly, we show that the prior finding is only replicated in the context of start‐ups and not established firms. Hence, women's responsiveness to gendered wording in job adverts appears to depend on the context. Implications are that incorporating feminine wording in job adverts is likely to be more effective in contexts where women anticipate greater challenges in becoming integrated into the workplace culture. Entrepreneurs' recruitment strategies designed to assemble a diverse workforce should therefore differ from those of established firms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".