Penalty or Payoff? Diversity of Tactics and Resource Mobilization Among Environmental Organizations
Bibliographic record
Abstract
Environmental nongovernmental organizations (ENGOs) need resources in order to mount campaigns and ensure their stability over time. What is the most effective strategy to attract these resources? Building upon research on social movement strategy and resource mobilization, we examine how a diversity of income sources, tactics, and issue focus affects three measures of mobilization outcomes: income, staff, and members. We examine these relationships using a unique panel data set of ENGOs in the UK collected by the Environmental Funders Network in 2013 and 2016. Our analyses show that using a more diverse range of tactics in wave 1 is associated with mobilizing more staff and members in wave 2. However, there is no significant penalty or payoff for groups that engage in issue or income diversification. We argue that diversifying tactics benefits organizations by helping them accrue the necessary human resources required for environmental action.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".