Your PDF contains <b>multiple lecture PPTs</b> from the GIAN course on <i>Migration, Gender, Labour, and International Standards</i>. The content spans historical, sociological, legal, and policy-oriented dimensions of migration.
Bibliographic record
Abstract
GIVEN DETAILS – CLEAN & SIMPLE SUMMARYYour PDF contains multiple lecture PPTs from the GIAN course on Migration, Gender, Labour, and International Standards. The content spans historical, sociological, legal, and policy-oriented dimensions of migration.Below is a structured summary of the main sections from the file.🧭 1. “Donkey Migration” – Illegal Migration from India📌 Key Points“Donkey route” = illegal, unsafe migration path used to reach USA, Canada, Europe.Popular especially in Punjab & Haryana.Migrants depend on agents, middlemen, “donkers”, and criminal networks.Routes involve jungles, deserts, oceans (e.g., Darien Gap).📌 Why People Choose Donkey MigrationLow wages in India; high wage expectations.Unemployment, poverty, corruption.Strong aspiration for Western lifestyle.Coaching centers, media glorification.Agents create “dream selling”.📌 ConsequencesInjury, death, exploitation, trafficking.No support when caught abroad.Loss of skilled youth from India.Diplomatic strains with destination countries.🧭 2. Migration of Nurses from Punjab📌 Global ContextWorldwide nursing shortage.India = 2nd largest supplier of migrant nurses to OECD.📌 Study Findings (303 nurses, Punjab)66% had B.Sc Nursing.Starting salary in private hospitals: ₹4,000–5,000 only.Majority aspire to migrate to Australia, Canada, USA.77% cite “better income & lifestyle” as reasons.Migration routes:Work permit (60%)Study visa / spouse visa as alternativeAgents charge ₹15–30 lakh.📌 Key InsightNursing migration from Punjab is becoming a family strategy supported by networks and agents.🧭 3. Gender & Migration – Feminist Perspectives📌 Important IdeasMigration is gendered — women face different risks and opportunities.Women migrant workers mostly in informal sector: domestic work, manufacturing, services.High vulnerability to:Sexual exploitationJob lossLack of social securityViolence & harassmentNo written contracts📌 ILO Convention 189Protects domestic workers, including migrants:Safe working conditionsWritten contractsSocial protectionProtection from violenceAccess to complaint mechanisms🧭 4. International Labour Standards (ILS) & Migration📌 Key ConventionsMigration for Employment Convention (C97)Migrant Workers Convention (C143)Private Employment Agencies Convention (C181)Domestic Workers Convention (C189)📌 Focus AreasEqual treatment of migrant & national workersPrevention of trafficking, forced labourSocial security protectionRegulation of private recruitment agencies🧭 5. Labour Codes of India – Migration Elements📌 Occupational Safety, Health & Working Conditions Code (OSH Code) 2020Expands definition of inter-state migrant worker.Provides:Toll-free helplinePortals and data collectionExperience certificatesSafety and health surveysAccommodation, crèche facilities📌 LimitationsNot fully gender-sensitiveImplementation challengesNeed for stronger protection for women migrants🧭 6. Historical Context – Slavery & Indentured Labour📌 SlaveryExisted globally; reached peak in trans-Atlantic trade.12.5 million Africans transported; 10.7 million survived.📌 Indentured LabourAfter slavery ended, British transported ~2 million Indians (1834–1917) to:MauritiusFijiTrinidad & TobagoSouth AfricaSri Lanka, MalaysiaConditions harsh; contracts misleading; high deaths on voyages.🧭 7. South Asian Migration – Colonial & Contemporary Trends📌 Colonial EraLabour migration: plantations, construction, military.Brokers/agents existed similar to today.📌 Modern EraMassive migration to GCC: Saudi Arabia, UAE, Kuwait, Oman.South Asia has world’s largest diaspora (India: 17.7 million).Push factors:UnemploymentInformal economyLow wagesPull factors:Gulf labour demandWestern opportunities
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.863 | 0.739 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".