Bibliographic record
Abstract
Non-governmental organizations (NGOs) face a broad spectrum of barriers to effective transnational cooperation (Ó Siochrú 2003). One critical barrier is the lack of ready access to software tools that facilitate transnational, multi-lingual, collaborative work. As an example, Civil Society's drafting processes for the World Summit on the Information Society (WSIS) have been very complicated, tedious, and prone to error. Complicating the process further is the fact that NGO communities are now often distributed across multiple languages. There are, for example, six official languages in WSIS. Truly democratic debate over document revisions is severely hampered if translations are not available. This then negatively impacts the sustainability of such processes.A number of content management systems now exist that might be extended and adapted for this purpose, but no fully functional system as such exists that is accessible to the majority of NGOs. A critical factor here is the use of a free software model. Proprietary solutions are usually prohibitively expensive and, thus, are neither accessible nor sustainable with the NGO community. This paper will present the context in which advanced collaboration tools for NGOs is needed. It will also discuss general system requirements and provide technical background.
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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.016 |
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".