OF MOOSE AND MAN: THE PAST, THE PRESENT, AND THE FUTURE OF HUMAN DIMENSIONS IN MOOSE RESEARCH
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is a gap between a growing interest to study the moose/human interface (MHI) and the actual effort made to understand this human dimension (HD) component in research. A content analysis of Alces 1974-2001 showed that the relative contribution of HD-papers increased until 1991 but decreased thereafter. Of 66 HD-articles published, 68% of the papers covered how man affects moose with hunting and collisions the single most important topics, and 15% were about values (economic and attitudes). Outside Alces, articles appeared that were underrepresented in Alces or in the Proceedings of the North American Moose Conference and Workshop. I identify four priority HD-areas for future studies: (1) how do people react to changing densities and distributions?; (2) which management alternatives are acceptable for managing the urban and suburban MHI and what makes them acceptable?; (3) how important are to non-consumptive users?; and (4) what are the population dynamics and attributes of the consumptive user and what makes important to consumptive users? A scientific challenge is to further merge ecological and social science to integrate this in management strategies. ALCES VOL. 39: 11-26 (2003)
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it