9 GIRLS ACTION NETWORK Reflecting on Systems Change through the Politics of Place
Bibliographic record
Abstract
In this chapter, we engage in a refl exive process of studying an organization for girls with which we have all been involved as adult women.While engaging in a refl exive exercise, we ask the following questions: What can we learn about networks as vehicles for change?What have we learned from facilitating a diverse network, and how have we come to know this?Where does this process take us?This chapter has two main sections.First, it presents the theoretical frameworks that have informed the growth, theory of change, and impact of the Girls Action Foundation (GAF) 1 and the Girls Action Network (GAN). 2 The second section identifi es politics of place within the network and refl ects on what has been learned through practice, in order to bett er understand how diverse networks can act as vehicles for social change.By analyzing the results of a recent evaluation (Fraser et al. 2013a) of the network alongside focus group discussions with Girls Action staff , we identify key issues and provide direction for moving forward.Our goal is to inform network theory and practice as well as to share knowledge with other girlhood scholars working to eff ect systems change in girls' lives.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".