Personal Social Support and Non-Support in Career Aspirations towards Senior Management amongst Women in Middle Management: Multiple Dimensions and Implications on Measurement
Bibliographic record
Abstract
Women middle managers aspiring for senior management execute their career choices in a unique career context. They experience the influence of personal social support and non-support in executing their aspirations towards senior management. Yet, it is observed that measures available to capture personal social support and non-support of this cohort of careerist are not adequately comprehensive. It was felt that there is a considerable space to develop a measure on personal social support and non-support as a contextual factor that affects career choice of women in middle management aspiring for senior management. This paper aims to highlight possible multiple dimensions of personal social support and non-support that affect career aspirations towards senior management amongst women in middle management. Concurrently, it offers some recommendations to develop a measure to capture aforementioned phenomena. To fulfill above aims, a targeted literature review on main areas under discussion was carried out in leading scientific databases such as EBSCOhost, JSTOR, ProQuest, Science Direct, and SpingerLink with the use of key words: women, career choice, aspirations, management, leadership, social support, significant others , and measurement. It is believed that development of a measure to capture the influence of personal social support and non-support in career aspirations towards senior management amongst women in middle management might contribute to enhance the quality of research conducted in this area and intervention programmes taken up to support female senior management aspirants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".