Bibliographic record
Abstract
Today's married employees are typically part of dual career couples. This makes it increasingly difficult for married female employees to find the time to fulfill the commitment to home, spouse, children, parents and friends. They are increasingly recognizing that work is infringing on their personal lives, and they are not happy about it. For example, recent studies suggest that employees want jobs that give them flexibility in their work schedules so that they can better manage work-life conflicts. Organizations that don't help their female employees achieve work-life balance will find it increasingly difficult to attract and retain the most capable and motivated employees. The present study intends to identify the major causes and remedies of work-life conflict which the working married women face in the current scenario. Married female professionals with children (n=100) were interviewed to examine the grave issues related to work-family conflict and HR remedies. It also intends to make the organizations realize the importance of family friendly work arrangements so as to have a joyful organization. Basic descriptive statistics were conducted and qualitative data from the interviews were evaluated. This data analysis produced a set of pie charts that illustrated respondents' concern for work-family balance.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".