Gender-specific assessment of natural resources using the pebble game.
Bibliographic record
Abstract
This dataset was moved to: https://doi.org/10.34725/DVN/29787Using a gender perspective to assess the preferences and values people associate with natural resources is essential, especially if the research aims to deepen understanding about men and women in relation to their natural environment. A game using pebbles has proven effective in classifying the value of natural resources, and the reasons behind the valuation. The pebble game is among many tools used in participatory rural appraisals (PRA). Sheil et al. (2002), for example, used the method to examine biological diversity in the context of landscape assessment. The pebble game was adapted in several gender researches in rural and migrant communities in Jambi, South and Southeast Sulawesi, Indonesia. These were supported by AgFor (Sulawesi Project funded by the Canadian International Development Agency) and REALU (Reducing Emission from Alternative Land Uses) projects. The studies assessed the importance of livelihood sources, the levels and nature of involvement of men and women in farming activities, the reasons for men and women preferences over natural resources, and the values they attach to them.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.021 |
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".