Why hunt upland game birds? Pheasant, grey partridge and sharp-tailed grouse hunter motivations, satisfaction and recreation specialization
Bibliographic record
Abstract
Upland game bird hunting is a popular outdoor recreation pursuit in Alberta, Canada yet little is known about the people who participate in the activity. The purpose of this thesis was to investigate the characteristics, satisfaction, and motivations of upland game bird hunters. Upland game bird hunters were examined through motivation orientations, the multi-satisfaction approach and the recreation specialization framework using data obtained from a sample of 452 individuals who hunted pheasant (Phasianus colchicus), grey partridge (Perdix perdix) or sharp- tailed grouse (Tympanuchus phasianellus) in Alberta during at least one season from 2015 to 2019. These approaches and differences between them framed two studies: The first study (Chapter 2) applied the multiple satisfaction approach in a new context by exploring the characteristics of released pheasant and wild upland game bird hunters. Motivation clusters were identified that included enthusiast, nature-sport and least engaged hunters. Results suggest that the motivations and satisfaction of hunters who pursued pen-raised and released pheasants did not differ from those who hunted wild birds. Hunting regulation strategies that increased the number of days available to hunt and promoted game species diversity provided the greatest levels of satisfaction. While harvest was a motivation of most hunters, non-harvest related motivations, including the opportunity to exercise, were most important. I propose that lifestyle experiences, rather than harvest alone form the fabric of hunter motivations. The second study (Chapter 3) applied the recreation specialization framework to characterize the different levels of involvement that prairie upland game bird hunters had to the activity. Three levels of upland game bird hunting involvement were identified: avid, intermediate and casual hunters. Low scores on the measures of centrality may suggest the secondary importance of upland game bird hunting to big game or waterfowl hunting. The ii findings demonstrate the multidimensionality of recreation specialization, that avid hunters demonstrated a greater commitment to the activity through association with a leisure social world, that avid hunters demonstrated greater perceived skill and knowledge, and that hunters with higher levels of specialization scored higher on harvest related dimensions. The overall findings, theoretical and practical implications, and limitations of these studies and future research suggestions are summarized in Chapter 4.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".