Owners and occupants: mapping the Blaan of Malbulen (Davao Occidental, Philippines)
Bibliographic record
Abstract
Since the proclamation of the Indigenous People’s Rights Act in 1997, the government of the Philippines has started to issue ancestral domain land titles, in an attempt to outline within its own legal framework how indigenous peoples should deal with land claims, disputes and ownership. Drawing on eleven months of fieldwork (2015–2019), this article describes the notions of ownership (fun) and occupancy (mnè) among the Blaan of Malbulen, and discusses whether their own views of the land are compatible with those proposed by the government. In fact, Blaan do not consider themselves to be the first inhabitants of the land. This status belongs to the fun spirits, its true owners, whose approval is needed to be accepted as an occupant, i.e. to build a house, to hunt, to cut down a tree or to cross a river. Humans are here believed to be mere occupants of the land. Places, humans and nonhuman beings are interwoven in ways that we have investigated through participatory and digital cartography. Mapping these beings reveals an interactive landscape. Its epistemology allows the anthropologist to question their own views and representations of space, and to understand how they may differ from local perspectives.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".