The study of effective communication in social awareness campaign through HIV/AIDs anti-discrimination & anti-stigmatisation campaigns, and exploring the factors behind effective communication.
Bibliographic record
Abstract
The 6th Singapore AIDS Conference in November 2008 was the first time Singapore put its focus in the issue surrounding discrimination and stigmatisation towards people living with HIV/AIDS, and this showed that more efforts will be put into the communication of this issue to the public in the future. Singapore has a lot to learn from other countries like Canada, United State and Hong Kong that had already established strong foundation towards this sensitive topic. Therefore, there is a need to establish an effective communication to address the issue of HIV/AIDS discrimination and stigmatisation. This is urgent because for many years our nation has been constantly advocating on the danger of contacting the virus and disease, as such, generating fear towards it. \nThis thesis will look into and analysis HIV/AIDS campaigns done in Singapore and successful HIV/AIDS anti-discrimination campaigns from other countries. And, find out the reason why HIV/AIDS sufferers were discriminated in our society. With the information gathered, appropriate theories in designs will be used to aid the process of creating a HIV/AIDS campaign that will communication the issue of discrimination and stigma in Singapore.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".