Bolstering Female Labour Force Participation Rates in India: Lessons from the Canadian Employment Equity Model
Bibliographic record
Abstract
Poor Female Labourforce Participation (hereinafter, ‘FLFP’) is a critical problem across the globe. The Indian FLFP rate has been steadily declining from 31.79% to 19.2% in 2021, making the country one of the worst performers globally. In the stratified Indian society, the intersection of gender, caste and religion makes FLFP a complex socio-legal issue that is yet to be confronted by the Indian Government in its entirety. In contrast, Canada has one of the highest FLFP rates worldwide with the Employment Equity Act (hereinafter, ‘EEA’) as a ‘proactive strategy’ to target systemic discrimination against women and other minority workers. The principal question that the thesis will address is whether the EEA model can be reimagined in the Indian context to eliminate systemic barriers against women and enhance the FLFP rate in India. To address the principal question, the thesis will first scrutinise the discrimination that confronts women workers with diverse identities in India and Canada. Second, the history and development of the EEA model and judicial discussion on employment discrimination and substantive equality will be traced. Third, the thesis will cull out key learnings for India from the successes and failures of the EEA model. Finally, the prevailing Indian quota system will be reimagined using the EEA model to travel beyond improving the statistical representation of women to foster an inclusive work environment which bolsters training and promotional prospects for women and dismantles occupational ghettos. The research methodology will be doctrinal and comparative as the thesis studies two multicultural, constitutionally similar, and common law-based polities. Considering the post-COVID decline in FLFP rates in both countries, the expansion of horizontal quota policy for women in public employment in India and the lack of comparative studies on special measures in both countries, the thesis becomes a timely and essential contribution to transforming organisational structures
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".