Mentoring, Career Plateau Tendencies, Turnover Intentions And Implications For Narrowing Pay And Position Gaps Due To Gender Structural Equations Modeling
Bibliographic record
Abstract
This study analyzed responses to career-related questions from a survey of experienced Canadian Certified Management Accountants (CMAs), relative experts in the field of management accounting, to address how mentoring affects turnover intentions and career plateau tendency of male and female accounting professionals in industry. In this regard, we used structural equations modeling to build and test a framework illustrating the impact of mentoring and career-related factors. Results indicate that fostering a mentoring environment within an organization can strengthen CMAs perceptions of their careers and employers. Mentoring has also been suggested to enhance womens opportunities to advance in organizations and help women break the glass ceiling. Analyses of data relating to compensation in 2007 and 2009 for a sample of female and male CEOs and operating performance of companies led by these CEOs for these years indicate that, that compensation gaps due to gender appear to be narrowing at the top management level.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".