The E-NGOs and their networks: the use of relationships in goal achievement
Bibliographic record
Abstract
In the society, the relation between actors, individuals or organisations, are daily. From these interactions, networks are rising. An actor gets closer to another and gets to exchange information, objects and other resources. These resources help to take action and to achieve contextual goals. The network appears as a place where people and organisations can get the tools that will allow to reach goals. Taking the perspective of the ENGOs, this thesis tends to develop understanding about how these organisations use their networks. It is about exploring the ENGOs' practices within their network to get to know their connections, the kind of resources they exchange and to understand if these relationships help to achieve goals such as the protection of the nature or the change of people's behaviours. The research is mainly based on the study of the theories of social network and social capital as well as the data collected at three environmental NGOs: Greenpeace (Swedish office in Stockholm), The Swedish Society for Nature Conservation (Head Quarter in Stockholm) and Surfrider Foundation Europe (Head Quarter in Biarritz, France).
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".