Economic overview of the Robson Valley Forest District
Bibliographic record
Abstract
The principal objectives of this study were to chronicle the hybrid approach to data collection adopted to formulate a regional economic overview of the Robson Valley Forest District (RVFD), and to summarize a suite of baseline economic indicators relevant to the forestry, visitor, public, and agriculture sectors in the RVFD region. Secondary data collection was augmented with primary data collected through formal and informal survey techniques. Data were collected for traditional economic indicators, such as gross revenue, employment, and income.The forestry sector is the largest contributor to the regional economy. It provides the highest estimates of average annual wage (ranging from $46,975 to $64,007), number of employment positions (574 jobs including full-time and part-time or seasonal positions), and total revenue ($74.1 million). The visitor sector, the second largest contributor, generates $18.1 million in revenues and 514 employment positions; however, the estimated average annual wage ($20,956) is the second lowest. The average annual wage in the public sector is the second highest at $25,669, with 350 employment positions existing in this sector. The agriculture sector is the smallest contributor, providing the lowest average annual wage ($19,145, or $17,420 inflated to 2001 dollars) and total revenue ($4.6 million). Despite the relatively lower contribution to the economy, the total number of farms has remained relatively stable.A comprehensive understanding of the mechanics of an economy facilitates decision making. This report provides a methodology for small region data collection and the resulting baseline information necessary for future assessment of responses within the economy to internal and external changes.
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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 | 0.000 |
| 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 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".