Bibliographic record
Abstract
Working in Southeast Asia as an academic and as a staff member for an environmental organization, I have witnessed non-fiction film being increasingly used for environmental advocacy and awareness-building, especially in the context of hydropower development. Such films seek to highlight the threats posed by hydropower to rivers and to the local residents who depend on them. However, my experiences with some of these films bring to light the degree to which the films themselves re-assert or produce specific claims about particular places and about development. As a geographer, I want to explore the cartographic possibilities and pitfalls of activist film projects.What cartographic stories does film tell? Are films, like maps, constitutive of technologies of power? As non-fiction film has been increasingly produced for environmental causes worldwide, I believe that “film-as-map” deserves interrogation. Accordingly, film is examined here as a medium through which non-governmental organizations (NGOs),activists and local residents collaboratively stake claims, construct boundaries and effectually re-map the river. I want to explore what ways film can offer marginalized groups an avenue to push for change or resistance against unjust development schemes (similar to countermapping strategies). Also, what are the disadvantages of using such strategies and how can film benefit from critiques and analysis from within the discipline of geography?
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".