Poking a sleeping bear: the challenge of organizational recruitment for controversial topics
Bibliographic record
Abstract
Researchers often approach employers to investigate employees’ work and family experiences. Organizational willingness to grant access to employees can vary, especially when the research topic is seen as controversial or contentious for the employer. This paper explores this methodological challenge using a research example from Manitoba, Canada, which explored the use of parental leave by male employees and the impact of managerial attitudes and corporate culture on usage. Sixty large employers were recruited with only seven of those organizations agreeing to participate. In this paper, the reasons organizations gave for declining to participate and the implications of their decisions for the research are examined. Although the final sample included 905 managers and employees, participating organizations tended to be employee-focused and family-friendly employers. Organizations declined participation for a variety of reasons: avoiding raising the issue with unions, awareness that their policies unfairly benefited female leave takers, and simply not seeing the relevance of a topic relating to men’s work–family experiences. A dialogue often absent from the literature, it is important to understand how employers can limit researchers’ access to employees on controversial topics. The existence of such barriers suggests alternative avenues to recruit participants directly when topics are contentious for employers.
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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 | 0.391 | 0.319 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.040 | 0.035 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".