Journal of Forest Business Research: a leading platform for advancing forest business and investment science research
Bibliographic record
Abstract
The Journal of Forest Business Research (JFBR), an international peer-reviewed and open-access journal, has been developed to offer a novel publication avenue for forest business research contributions. This effort has been motivated by the realization that there were no dedicated forest business scientific journals in existence and the need to have a scientific journal to support growing volume of forest business research. The journal aims to effectively meet the needs of contributors and readers by bringing together academic and professional business research in forestry. The following section describes why there is a need for the JFBR and what makes this journal a leading platform for advancing forest business and investment science research. Then, we summarize all the papers included in our two issues in 2023. This year, we delivered to hands of our readers over 340 pages of high-quality forest business and investment science research. The articles published in 2023 discussed, among others, forest carbon and its contribution to total timberland investment returns, capital investment and annual expenditures related to forests in the United States (U.S.), wood pellet manufacturing industry from residents’ perspectives in the U.S. South, discount rates in forest management decisions, the effect of various COVID-19 policies on standing timber prices in the U.S. South, the relationships between innovation constructs and demographic and management attributes of wood furniture firms in Kenya, the economic feasibility of silviculture investments to reduce butt rot and ungulate browse damage in Canada, the sustainability of the production, processing, and exporting systems of frankincense (Boswellia papyrifera) in Ethiopia, and the development of the Iranian wood products industry over the past two decades. All these articles truly show the international character of forest business research. In the final section, we indicate what types of articles we are seeking and how you can support our efforts.
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 | 0.028 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.051 | 0.023 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.105 | 0.076 |
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".