A Review of Diversity, Equity, and Inclusion Themes in Arboriculture Organizations’ Codes of Ethics
Bibliographic record
Abstract
Abstract Codes of ethics (COEs) play an important role in outlining an association’s ethical expectations of its membership. Diversity, equity, and inclusion issues in arboriculture have been long-standing, resulting in prevalent systemic inequality and discrimination within the industry. Codes of ethics may provide a means through which to address systemic barriers; however, unlike the forestry industry, there is limited understanding of how arboriculture organizations’ codes of ethics approach diversity, equity, and inclusion. This review of 9 national and international arboriculture organizations’ codes of ethics examines how equity, diversity, and inclusion are included within the expected ethical conduct of professional members. Through thematic and qualitative content analyses, we found that arboriculture organizations’ codes of ethics varied in length and depth, ranging from 7 to 47 statements in codes of ethics. Most ethical codes were positively framed, indicating what membersshoulddo, rather than the contrasting negative framing which indicates what membersshould notdo. Of the 9 arboriculture organizations, 7 included equity, diversity, and inclusion statements. Inclusion codes were the most common (n= 6 COEs), followed by equity (n= 5 COEs) and diversity (n= 3 COEs). In total, 8 codes of ethics referenced adherence to laws and regulations, 4 of which may provide a means for promoting ethical practice in the absence of explicit statements about equity, diversity, and inclusion.
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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 | 0.048 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".