Paper presented to 26th International Sunbelt Social Network Conference, Vancouver. ***DRAFT – PLEASE DO NOT QUOTE WITHOUT PERMISSION OF AUTHORS***
Bibliographic record
Abstract
Environmental activist groups are active users of the Internet, a relatively low- cost medium with global reach that can facilitate the formation of global virtual communities (thus compensating for a lack of critical mass of activists in a given country). Web data are therefore very useful for social science research into the mobilisation and coordination of environmental activist groups. However such data are vastly different in scale and nature to the data usually studied by empirical social scientists and therefore require the development of new methods and approaches. We outline a new approach for studying online activist networks that draws upon methods from both the information and social sciences. A particular focus of our work is the use of web data to identify the emergence of an antinanotechnology theme in online environmental networks, a topic that is explored more fully in a companion paper (O’Neil and Ackland, 2006a). This paper details our preliminary work on choosing key activist websites (“seed sites”), identifying networks via web mining for hyperlinks to and from these sites, data cleaning, categorisation of websites and analysis of both network structure and web page content.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.206 | 0.041 |
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".