Bibliographic record
Abstract
Research on sustainability science has been concerned with pointing the way towards a sustainable society. On a global scale, sustainability is seen as depending on three systems: the global system, the human system and the social system. In the social system, the need to address issues of social sustainability, including literacy, education, malnutrition, child mortality, and gender empowerment, as well as its connections with human and global sustainability, has given rise to the eight Millennium Development Goals, which break down into twenty one quantifiable targets that are measured by sixty indicators. Therefore, it is clear that the problems and issues associated with the achievement of these goals are very complex to be addressed by a single discipline and that community informatics (CI) may have an important role to play in interdisciplinary efforts to address these goals. Against this backdrop, one of the first challenges is to put the notion of a social inclusion system (a system to promote social sustainability) in more precise terms. In this direction, the purpose of this paper is to discuss and present an initial ontology to describe social inclusion systems. While ontological development in sustainability science has emphasized a problem-solution approach, we believe that the issues of social inclusion will be more naturally addressed by a situation-transformation approach, which is the focus of our ontology.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".