Could policymakers do more to eliminate cumulative poverty sustainably, in the battle against global poverty? A social marketing approach- An empirical study
Bibliographic record
Abstract
Purpose: The paper investigates four fundamental research questions: (i) What more could global policymakers do to eliminate cumulative poverty sustainably? (ii) How do we adopt an inclusive approach driven by grass roots levels? (iii) How do we strengthen/broaden support for existing poverty intervention mechanisms worldwide? (iv) What is social marketing technique (SMT) and how would SMT help eradicate poverty/sustainability? Design/methodology: A mixed-methods research design engaging qualitative and quantitative approaches; involving 254 respondents; aged 18 years and older from 24 countries and five continents were surveyed at Regent College London. Results/Findings: The study revealed global citizens of all ages and support for the needs of global communities across the world are inadequately addressed. A great deal of change/more is needed. Global policymakers could do more. Practical implications: This study is aimed at enhancing/expanding or strengthening existing or previous global poverty reduction and sustainability interventions. Conclusions: Co-authors argue highlighting additional/alternative strategies for global policymakers, inclusive approaches driven by grass roots levels, and utilising persuasive social marketing techniques are likely to enhance the probability of ending cumulative poverty sustainably.
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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".