Communication for all and the Sustainable Development Goals
Bibliographic record
Abstract
PURPOSE: Communication is central to the accomplishment of each of the United Nations' 17 Sustainable Development Goals (SDGs) and is a fundamental human right. METHOD: (IJSLP, vol. 25, no. 1) is dedicated to communication, swallowing and the SDGs; particularly focussing on people with communication and/or swallowing disability and those who support them. RESULT: The papers in the special issue of IJSLP demonstrate that successful communication is necessary for realisation of all 17 SDGs at both a global and an individual level and advance the international call for SDG 18: Communication for All. The 36 papers address all 17 goals, focussing on poverty, hunger, health, education, work, innovation, climate, cities, land, oceans, justice, and partnerships. Authors worked and undertook their research in Australia, Austria, Benin, Cambodia, Cameroon, Canada, China, Columbia, Denmark, Egypt, Ethiopia, Ghana, Greece, Iceland, India, Iraq, Ireland, Italy, Jordan, Kenya, Lebanon, Maldives, Mozambique, Nepal, New Zealand, Nigeria, State of Palestine, Peru, Philippines, Rwanda, Serbia, South Africa, Uganda, UK, USA, Vietnam. CONCLUSION: Communication for all is essential for the achievement of the SDGs, "peace and prosperity for people and the planet" (United Nations, 2015a). Achievement of the SDGs is the role of all; including communication specialists, people with communication/swallowing disability, their families and communities.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".