The Structure of Social and Environmental Accounting Research: A <scp>Citation‐Based</scp> Social Network Analysis*
Bibliographic record
Abstract
ABSTRACT We investigate the structure and progress of the emergent field of social and environmental accounting (SEA) research by combining social network analysis (SNA) with a systematic literature review based on 461 papers containing 26,872 citations in total. Our approach, which to the best of our knowledge this study is the first to apply, enables us to use research metrics to identify the knowledge database of SEA research and examine how the field has been evolving. We analyze the connections and structure of foundational SEA research spanning the period 1973–2015. In line with previous research, our results reveal network characteristics typical of an emerging field—that is, subgroups without distinguishing content characteristics and low network density. Our additional analyses show that the results remain unchanged until 2021. The state‐of‐the‐art SNA we apply, the knowledge database we identify, and the insights into the SEA field's structure that we provide support the growth of the field.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.059 | 0.065 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".