Pakistan - Packages Limited Manufacturing : Advancing Gender Diversity through Equal Opportunity Recruitment Practices : Case Study
Bibliographic record
Abstract
Pakistan’s economy is presently growing at just under 6 percent annually. The country’s GDP could grow by 30 percent if women and men participated equally in the workforce. Women’s education levels in Pakistan have increased over time. Yet, only a quarter of women are working compared to over 80 percent of men. The gap is staggering and closing it represents a huge economic opportunity for Pakistan. In addition to Pakistan’s economy, closing gender gaps is also good for Pakistan’s businesses. The competitiveness and growth of Pakistan’s firms could be significantly enhanced if firms could bring into their company diverse skills and talents, which are urgently needed to be more productive and innovative. Despite competition over attracting and retaining skilled employees, few companies have widened their search for talent and skills by tapping into Pakistan’s vastly underutilized female talent pool. A 2017 study by The Pakistan Business Council’s (PBC) Centre of Excellence in Responsible Business (CERB) found that women represent less than 20 percent of the workforce of 90 percent of the surveyed PBC member firms. Although this is higher than the national average of less than 10 percent women in the formal, private sector workforce, in most cases, women continue to be underrepresented in formal employment and earn less than men for equivalent work. Pakistan’s private sector has a critical role to play in ensuring that women have access to more and better jobs. A growing number of forward-looking Pakistani companies are now starting to see the business case for gender diversity, even in sectors considered non-traditional for women. To expand this awareness and turn it into action, IFC partnered with PBC to bring to Pakistan IFC ‘s global research and advisory expertise on the business case and good practices for advancing women’s employment These case studies are part of a series of initiatives that IFC and PBC have jointly led over the past year to mobilize more companies in Pakistan to increase gender diversity in their workforce. The aim of the case studies is to provide guidance to Pakistani firms on successful approaches for recruiting, retaining, and promoting more women and achieving better business outcomes. The case studies feature five PBC member companies that are promoting greater inclusion of women in their workforce in five thematic areas: Artistic Milliners Limited for the Women in Business Leadership theme, HBL for the Anti-Sexual Harassment theme, Interloop Limited for the Women in Non-Traditional Roles theme, National Foods Limited for the Family-Friendly Policies theme, and Packages Limited for the Equal Opportunity Recruitment Practices theme. HBL and Packages are IFC investment clients and Artistic Milliners and Interloop are IFC advisory clients.
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 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".